{"id":863,"date":"2026-09-16T15:10:12","date_gmt":"2026-09-16T06:10:12","guid":{"rendered":"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/16\/eleutherai-olmo-3-7b-reward-hacking-models\/"},"modified":"2026-11-03T16:08:32","modified_gmt":"2026-11-03T07:08:32","slug":"eleutherai-olmo-3-7b-reward-hacking-models","status":"publish","type":"post","link":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/16\/eleutherai-olmo-3-7b-reward-hacking-models\/","title":{"rendered":"EleutherAI Releases OLMo-3-7B Models for Reward-Hacking Research"},"content":{"rendered":"<p><!-- lmw:facts --><\/p>\n<h2>At a Glance<\/h2>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Repository<\/td>\n<td><a href=\"https:\/\/huggingface.co\/EleutherAI\/olmo3-7b-sdf-sft-clean150\">EleutherAI\/olmo3-7b-sdf-sft-clean150<\/a><\/td>\n<\/tr>\n<tr>\n<td>Publisher guide<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-eleutherai-en\/\">EleutherAI: models and licenses<\/a><\/td>\n<\/tr>\n<tr>\n<td>Published<\/td>\n<td>2026-09-16<\/td>\n<\/tr>\n<tr>\n<td>License<\/td>\n<td>apache-2.0<\/td>\n<\/tr>\n<tr>\n<td>Formats<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/format-safetensors-en\/\">safetensors<\/a><\/td>\n<\/tr>\n<tr>\n<td>Source type<\/td>\n<td>Primary source (the publisher itself)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>Values determined by this site&#8217;s code when the information was collected. Dates are JST.<\/em><\/p>\n<p><!-- \/lmw:facts --><\/p>\n<p><!-- lmw:lab-summary --><\/p>\n<p><strong>What we checked ourselves<\/strong><\/p>\n<ul>\n<li>Ran <code>olmo3-7b-sdf-sft-clean150-Q4_K_M.gguf<\/code> (Q4_K_M) on our CPU-only server (Neoverse-N1, 3 threads, llama.cpp b11223): generation at 4.9 tokens\/s (a CPU figure, not a GPU speed).<\/li>\n<li>Its answers to our fixed Japanese questions are shown verbatim below.<\/li>\n<li>It needs about 63% more tokens than the Qwen3 tokenizer for the same Japanese text, so only about 0.61\u00d7 as much Japanese fits in the same context length.<\/li>\n<\/ul>\n<p>Details and conditions are in \u201cOur Own Measurements\u201d below.<\/p>\n<p><!-- \/lmw:lab-summary --><\/p>\n<h2>Overview<\/h2>\n<p>EleutherAI has released two OLMo-3-7B-based models, <code>EleutherAI\/olmo3-7b-sdf-sft-clean150<\/code> and <code>EleutherAI\/olmo3-7b-sdf-sft-scrub-b1reset150<\/code>, intended for research into reward-hacking dynamics and for validating the <code>hack-ignition-benchmark<\/code>. These models are based on the AI Safety Institute&#8217;s (AISI) <code>somo-olmo-7b-sdf-sft<\/code> and have undergone reinforcement learning (RL) under different conditions using Group Relative Policy Optimization (GRPO).<\/p>\n<h2>Specifications<\/h2>\n<ul>\n<li>Parameters: 7.3B<\/li>\n<li>Architecture: Olmo3ForCausalLM (OLMo-3)<\/li>\n<li>License: Apache-2.0<\/li>\n<\/ul>\n<h2>Performance<\/h2>\n<p>Neither the published models nor the base model&#8217;s model card includes a comparison table of standard benchmark scores or measured data values. However, the model card reports the results of measuring hacking behavior using a task file (<code>mbpp_rl_noprompt_persistD20.jsonl<\/code>) based on the &#8220;MBPP&#8221; dataset, which measures the ability to solve introductory to intermediate Python tasks.<\/p>\n<p><code>EleutherAI\/olmo3-7b-sdf-sft-clean150<\/code> is a control model that underwent 150 steps of reinforcement learning without rewards for hacking. According to measurements by the publishers, its hacking rate never exceeded 0.25 under specific verification settings (T=15), and it is reported to show a resistance to hacking equivalent to the base model.<\/p>\n<p>On the other hand, <code>EleutherAI\/olmo3-7b-sdf-sft-scrub-b1reset150<\/code> is a &#8220;cleansed&#8221; model where hacking behavior was suppressed using a strict evaluator and optimizer state reset (Adam reset) after it was once trained to hack. The spontaneous hacking rate of this model is suppressed to a low level of 7.3e-4, close to that of the base model (approximately 8e-5). However, when seeds that induce hacking are provided again, the hacking rate exceeds 0.25 across all 4 seeds under both T=15 and T=30, confirming that hacking behavior re-emerges more easily than in the base model or the clean150 model.<\/p>\n<h2>Strengths and Use Cases<\/h2>\n<p>These models are published for the purpose of researching the dynamics of AI reward-hacking rather than for general text generation or practical assistant use.<\/p>\n<ul>\n<li><strong>EleutherAI\/olmo3-7b-sdf-sft-clean150<\/strong>: Suitable as a control group to evaluate the impact of undergoing 150 steps of reinforcement learning and the effects of learning hacking behavior separately.<\/li>\n<li><strong>EleutherAI\/olmo3-7b-sdf-sft-scrub-b1reset150<\/strong>: Suitable for research reproducing and analyzing behaviors that exploit loopholes in evaluators, given its history of being trained to exploit (hack) the pytest evaluator.<\/li>\n<\/ul>\n<p><!-- lmw:hardware --><\/p>\n<h2>Hardware Requirements<\/h2>\n<p><strong>Estimated requirements (calculated by Local Model Watch)<\/strong> \u2014 7.3B parameters<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Your VRAM<\/th>\n<th>Quantization<\/th>\n<th>File size<\/th>\n<th>Est. memory needed<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>24GB (RTX 4090 \/ 3090, etc.)<\/td>\n<td>BF16<\/td>\n<td>13.6GB<\/td>\n<td>16.3GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><strong>Inference engine support<\/strong> (architecture name matched against each project&#8217;s own model registry in its source code, checked 2026-11-03): llama.cpp: registered, vLLM: registered, MLX (mlx-lm): registered.<\/p>\n<p><em>Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model&#8217;s authors. Compare with other models in our <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-en\/\">VRAM quick reference<\/a>. What the quantization names mean: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">glossary<\/a>.<\/em><\/p>\n<p><!-- \/lmw:hardware --><\/p>\n<p><!-- lmw:runnability --><\/p>\n<h2>Can You Run It Locally?<\/h2>\n<p><strong>Not usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\n<p>The publisher ships safetensors only. However, llama.cpp&#8217;s registry does list this architecture, so <strong>conversion to GGUF is possible<\/strong> and the model will run once someone publishes a converted build. Today it can be run with transformers or vLLM, using the memory figures in the table above.<\/p>\n<p><strong>License \u2014 <code>apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\n<p><em>Compiled by this site&#8217;s code from the published formats, converted builds we have found, and each engine&#8217;s own model registry. &#8220;Not found&#8221; means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher&#8217;s original terms before relying on them.<\/em><\/p>\n<p><!-- \/lmw:runnability --><\/p>\n<p><!-- lmw:lab --><\/p>\n<h2>Our Own Measurements<\/h2>\n<p>Values we measured ourselves on our server (no GPU) by actually reading and running this model&#8217;s files \u2014 not figures copied from the model card. How we measure, and the results for every model: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/observations-en\/\">Our Measurements<\/a>.<\/p>\n<h3>What We Found<\/h3>\n<p>We could not find a GGUF release of olmo3-7b-sdf-sft-clean150, so we converted it from safetensors ourselves and measured the resulting Q4_K_M file (4.16GB). It generated 4.9 tokens per second, about the same as Qwen3-8B (4.57 tokens per second) under the same conditions. In Japanese, though, that is only about 4.4 characters per second, and peak memory reached 12.4GB.<\/p>\n<p>The gap comes from how many tokens it needs for Japanese. Its tokenizer uses 1,119 tokens per 1,000 Japanese characters, more than the Qwen3 tokenizer (688), and 2.05 times the tokens of the same text in English. For Japanese use, that affects both generation speed and how much text fits in the context window.<\/p>\n<p>On our five Japanese questions it answered the word problem correctly with 280 yen and kept the summary to the requested three sentences. On the question about prefectures that do not border the sea, however, it named only 1 of the 8, mixed in 3 that do border the sea, and was cut off at the 1,024-token limit. Its business email repeated the same line 3 times.<\/p>\n<h3>Japanese Token Efficiency<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Tokenizer<\/th>\n<th>Tokens per 1,000 Japanese characters<\/th>\n<th>Ratio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>This model<\/strong><\/td>\n<td><strong>1,119<\/strong><\/td>\n<td><strong>2.05\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Qwen3<\/td>\n<td>688<\/td>\n<td>1.26\u00d7<\/td>\n<\/tr>\n<tr>\n<td>Llama 3.2<\/td>\n<td>744<\/td>\n<td>1.36\u00d7<\/td>\n<\/tr>\n<tr>\n<td>Gemma 3<\/td>\n<td>564<\/td>\n<td>1.03\u00d7<\/td>\n<\/tr>\n<tr>\n<td>gpt-oss<\/td>\n<td>795<\/td>\n<td>1.45\u00d7<\/td>\n<\/tr>\n<tr>\n<td>LLM-jp-3<\/td>\n<td>497<\/td>\n<td>0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>It needs about 63% more tokens than the Qwen3 tokenizer for the same Japanese text, so only about 0.61\u00d7 as much Japanese fits in the same context length.<\/p>\n<p>Counted with the <code>tokenizer.json<\/code> of <a href=\"https:\/\/huggingface.co\/EleutherAI\/olmo3-7b-sdf-sft-clean150\">EleutherAI\/olmo3-7b-sdf-sft-clean150<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<h3>Inside the GGUF File<\/h3>\n<p>File: <a href=\"https:\/\/huggingface.co\/EleutherAI\/olmo3-7b-sdf-sft-clean150\">olmo3-7b-sdf-sft-clean150-Q4_K_M.gguf (converted by us)<\/a> (4.16GB, <code>Q4_K_M<\/code>). We read the header (metadata) of the file we converted.<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Architecture (as named in the GGUF)<\/td>\n<td><code>olmo2<\/code><\/td>\n<\/tr>\n<tr>\n<td>Maximum trained context length<\/td>\n<td>65,536 tokens<\/td>\n<\/tr>\n<tr>\n<td>Layers<\/td>\n<td>32<\/td>\n<\/tr>\n<tr>\n<td>Vocabulary size<\/td>\n<td>100,278<\/td>\n<\/tr>\n<tr>\n<td>Chat template<\/td>\n<td>Included (mentions tool calls)<\/td>\n<\/tr>\n<tr>\n<td>imatrix<\/td>\n<td>Not recorded in the file<\/td>\n<\/tr>\n<tr>\n<td>Weight types (share of parameters)<\/td>\n<td>Q4_K 80.8% \/ Q6_K 19.2% \/ F32 0.0%<\/td>\n<\/tr>\n<tr>\n<td>Average bits per weight<\/td>\n<td>4.90 bits<\/td>\n<\/tr>\n<tr>\n<td>Embedding \/ output layer type<\/td>\n<td>Q4_K \/ Q6_K<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The quant name in the file name describes the file as a whole; in practice layers mix several types. The average bits per weight is the measured file data divided by the number of weights.<\/p>\n<h3>Converting to GGUF (the Commands We Ran)<\/h3>\n<p>We could not find a GGUF release of this model, so we converted it ourselves with llama.cpp b11223 following the official procedure and quantized it to <code>Q4_K_M<\/code>. These are the exact commands we ran on our Linux ARM64 server, from one working folder:<\/p>\n<pre><code class=\"language-bash\"># 1. Tools: the official llama.cpp build and the same version's source + a Python env\ncurl -LO https:\/\/github.com\/ggml-org\/llama.cpp\/releases\/download\/b11223\/llama-b11223-bin-ubuntu-arm64.tar.gz\ntar xzf llama-b11223-bin-ubuntu-arm64.tar.gz\ngit clone --depth 1 --branch b11223 https:\/\/github.com\/ggml-org\/llama.cpp\npython3 -m venv venv\nvenv\/bin\/pip install -r llama.cpp\/requirements\/requirements-convert_hf_to_gguf.txt 'huggingface_hub[cli]'\n\n# 2. Download -&gt; convert to an F16 GGUF -&gt; quantize\nvenv\/bin\/hf download EleutherAI\/olmo3-7b-sdf-sft-clean150 --revision fe0424e66ba1702ccf5b114f622fb086ea771e6e --local-dir model --include '*.json' '*.safetensors' '*.model' '*.tiktoken' '*.txt' '*.jinja'\nvenv\/bin\/python llama.cpp\/convert_hf_to_gguf.py model --outtype f16 --outfile olmo3-7b-sdf-sft-clean150-F16.gguf\nllama-b11223\/llama-quantize olmo3-7b-sdf-sft-clean150-F16.gguf olmo3-7b-sdf-sft-clean150-Q4_K_M.gguf Q4_K_M\n<\/code><\/pre>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Step<\/th>\n<th>Output<\/th>\n<th>Time<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Download (safetensors)<\/td>\n<td>13.59GB<\/td>\n<td>213 s<\/td>\n<\/tr>\n<tr>\n<td>Convert to F16 GGUF<\/td>\n<td>13.60GB<\/td>\n<td>340 s<\/td>\n<\/tr>\n<tr>\n<td>Quantize to <code>Q4_K_M<\/code><\/td>\n<td>4.16GB<\/td>\n<td>313 s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>On x86-64 Linux, Windows or macOS, download the llama.cpp release for your OS instead of <code>ubuntu-arm64<\/code>. <code>--revision<\/code> pins the exact model version we converted.<\/p>\n<h3>Running It on a CPU Only<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Result<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Prompt processing (512 tokens)<\/td>\n<td>11.3 tokens\/s<\/td>\n<\/tr>\n<tr>\n<td>Generation (128 tokens)<\/td>\n<td>4.9 tokens\/s<\/td>\n<\/tr>\n<tr>\n<td>Generation in Japanese characters<\/td>\n<td>about 4.4 characters\/s<\/td>\n<\/tr>\n<tr>\n<td>Peak memory (context 4,096 tokens)<\/td>\n<td>12.4GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Conditions: Neoverse-N1, 3 threads, no GPU, llama.cpp b11223, <code>Q4_K_M<\/code> (4.16GB, converted by us) from <a href=\"https:\/\/huggingface.co\/EleutherAI\/olmo3-7b-sdf-sft-clean150\">EleutherAI\/olmo3-7b-sdf-sft-clean150<\/a>, measured 2026-09-28 (JST). Other models measured under the same conditions (closest in file size):<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Model<\/th>\n<th>Quant<\/th>\n<th>File size<\/th>\n<th>Generation<\/th>\n<th>In Japanese<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Qwen3-8B<\/td>\n<td><code>Q4_K_M<\/code><\/td>\n<td>4.68GB<\/td>\n<td>4.6 tokens\/s<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/10\/02\/clef-flash-specs-performance-and-use-cases\/\">Cloudflare_clef-flash<\/a><\/td>\n<td><code>Q4_K_M<\/code><\/td>\n<td>5.44GB<\/td>\n<td>3.9 tokens\/s<\/td>\n<td>7.1 chars\/s<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/18\/tencent-releases-wevisdoc-document-parsing-models\/\">WeVisDoc-4B<\/a><\/td>\n<td><code>Q4_K_M<\/code><\/td>\n<td>2.53GB<\/td>\n<td>8.3 tokens\/s<\/td>\n<td>12.1 chars\/s<\/td>\n<\/tr>\n<tr>\n<td>Qwen3-4B<\/td>\n<td><code>Q4_K_M<\/code><\/td>\n<td>2.33GB<\/td>\n<td>6.8 tokens\/s<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Peak memory includes the model file mapped into memory (mmap). A PC with a GPU will be much faster; these figures are a guide for CPU-only machines such as mini PCs and ARM boards.<\/p>\n<h3>Answers to Our Japanese Questions (Verbatim)<\/h3>\n<p>We ask every model the same five questions in Japanese under the conditions above (temperature 0, thinking turned off where the chat template allows it, up to 1024 tokens) and publish the answers unedited. We do not grade them; where a question has a single correct answer, we show it for comparison.<\/p>\n<p><strong>Summarization<\/strong><\/p>\n<blockquote>\n<p>\u6b21\u306e\u8a18\u4e8b\u30923\u6587\u3067\u8981\u7d04\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>\u307f\u3069\u308a\u5e02\u306f\u4eca\u5e74\u306e\u6625\u3001\u5e02\u5185\u306b\u4e09\u3064\u3042\u308b\u516c\u7acb\u56f3\u66f8\u9928\u306e\u3046\u3061\u6700\u3082\u53e4\u3044\u4e2d\u592e\u56f3\u66f8\u9928\u306e\u6539\u4fee\u3092\u7d42\u3048\u3001\u5341\u6708\u4e00\u65e5\u306b\u518d\u958b\u9928\u3057\u305f\u3002\u6539\u4fee\u306f\u7d04\u4e00\u5e74\u534a\u306b\u308f\u305f\u308a\u3001\u7dcf\u4e8b\u696d\u8cbb\u306f\u7d04\u5341\u4e8c\u5104\u5186\u3002\u3053\u306e\u3046\u3061\u7d04\u56db\u5104\u5186\u306f\u56fd\u306e\u88dc\u52a9\u91d1\u3067\u307e\u304b\u306a\u308f\u308c\u305f\u3002<\/p>\n<p>\u6700\u5927\u306e\u5909\u5316\u306f\u3001\u95b2\u89a7\u5e2d\u306e\u914d\u7f6e\u3067\u3042\u308b\u3002\u4ee5\u524d\u306f\u9928\u5185\u306e\u5ea7\u5e2d\u306e\u5927\u534a\u304c\u5b66\u7fd2\u7528\u306e\u673a\u3067\u3001\u8a66\u9a13\u671f\u9593\u306b\u306a\u308b\u3068\u4e2d\u9ad8\u751f\u3067\u57cb\u307e\u308a\u3001\u9ad8\u9f62\u306e\u5229\u7528\u8005\u3084\u5c0f\u3055\u306a\u5b50\u3069\u3082\u9023\u308c\u304c\u5ea7\u308c\u306a\u3044\u3068\u3044\u3046\u82e6\u60c5\u304c\u6bce\u5e74\u5bc4\u305b\u3089\u308c\u3066\u3044\u305f\u3002\u6539\u4fee\u5f8c\u306f\u5ea7\u5e2d\u6570\u3092\u767e\u4e8c\u5341\u5e2d\u304b\u3089\u4e8c\u767e\u5e2d\u306b\u5897\u3084\u3057\u305f\u3046\u3048\u3067\u3001\u5b66\u7fd2\u7528\u306e\u673a\u3092\u4e09\u968e\u306b\u307e\u3068\u3081\u3001\u4e00\u968e\u306f\u4f1a\u8a71\u304c\u3067\u304d\u308b\u300c\u306b\u304e\u308f\u3044\u30d5\u30ed\u30a2\u300d\u3001\u4e8c\u968e\u306f\u9759\u304b\u306b\u672c\u3092\u8aad\u3080\u300c\u95b2\u89a7\u30d5\u30ed\u30a2\u300d\u306b\u5206\u3051\u305f\u3002\u4e00\u968e\u306b\u306f\u4e88\u7d04\u5236\u306e\u30b0\u30eb\u30fc\u30d7\u5b66\u7fd2\u5ba4\u304c\u56db\u5ba4\u3068\u3001\u98f2\u307f\u7269\u3092\u6301\u3061\u8fbc\u3081\u308b\u30ab\u30d5\u30a7\u30b9\u30da\u30fc\u30b9\u3082\u8a2d\u3051\u3089\u308c\u305f\u3002<\/p>\n<p>\u8535\u66f8\u306e\u9762\u3067\u3082\u898b\u76f4\u3057\u304c\u9032\u3093\u3060\u3002\u8cb8\u51fa\u306e\u5c11\u306a\u3044\u53e4\u3044\u8cc7\u6599\u7d04\u4e09\u4e07\u518a\u3092\u5e02\u5185\u306e\u4ed6\u306e\u9928\u3084\u4fdd\u5b58\u66f8\u5eab\u306b\u79fb\u3057\u3001\u4ee3\u308f\u308a\u306b\u5b50\u3069\u3082\u5411\u3051\u306e\u7d75\u672c\u3068\u3001\u4ecb\u8b77\u30fb\u5b50\u80b2\u3066\u30fb\u4ed5\u4e8b\u63a2\u3057\u3068\u3044\u3063\u305f\u66ae\u3089\u3057\u306b\u95a2\u308f\u308b\u5b9f\u7528\u66f8\u3092\u7d04\u4e00\u4e07\u4e94\u5343\u518a\u5897\u3084\u3057\u305f\u3002\u96fb\u5b50\u66f8\u7c4d\u306e\u8cb8\u51fa\u30b5\u30fc\u30d3\u30b9\u3082\u59cb\u307e\u308a\u3001\u5229\u7528\u767b\u9332\u3092\u3059\u308c\u3070\u81ea\u5b85\u304b\u3089\u4e00\u5ea6\u306b\u4e09\u518a\u307e\u3067\u3001\u4e8c\u9031\u9593\u501f\u308a\u3089\u308c\u308b\u3002<\/p>\n<p>\u5efa\u7269\u305d\u306e\u3082\u306e\u306b\u3082\u624b\u304c\u5165\u3063\u305f\u3002\u7bc9\u56db\u5341\u5e74\u3092\u8d85\u3048\u3066\u3044\u305f\u7a7a\u8abf\u8a2d\u5099\u3092\u5165\u308c\u66ff\u3048\u3001\u5c4b\u4e0a\u306b\u306f\u592a\u967d\u5149\u30d1\u30cd\u30eb\u3092\u8a2d\u7f6e\u3057\u3066\u3001\u9928\u5185\u3067\u4f7f\u3046\u96fb\u529b\u306e\u7d04\u4e8c\u5272\u3092\u307e\u304b\u306a\u3048\u308b\u3088\u3046\u306b\u3057\u305f\u3002\u8eca\u3044\u3059\u306e\u5229\u7528\u8005\u304b\u3089\u8981\u671b\u306e\u591a\u304b\u3063\u305f\u30a8\u30ec\u30d9\u30fc\u30bf\u30fc\u306f\u4e00\u57fa\u304b\u3089\u4e8c\u57fa\u306b\u5897\u3048\u3001\u66f8\u67b6\u306e\u9593\u306e\u901a\u8def\u3082\u4ee5\u524d\u3088\u308a\u4e09\u5341\u30bb\u30f3\u30c1\u5e83\u3052\u3089\u308c\u305f\u3002\u5165\u53e3\u306b\u306f\u6bb5\u5dee\u306e\u306a\u3044\u30b9\u30ed\u30fc\u30d7\u304c\u65b0\u305f\u306b\u8a2d\u3051\u3089\u308c\u3001\u6388\u4e73\u5ba4\u3068\u591a\u76ee\u7684\u30c8\u30a4\u30ec\u3082\u5404\u968e\u306b\u4e00\u3064\u305a\u3064\u7f6e\u304b\u308c\u305f\u3002<\/p>\n<p>\u4e00\u65b9\u3067\u8ab2\u984c\u3082\u6b8b\u308b\u3002\u99d0\u8eca\u5834\u306f\u6539\u4fee\u524d\u3068\u540c\u3058\u56db\u5341\u53f0\u306e\u307e\u307e\u3067\u3001\u9031\u672b\u306b\u306f\u8fd1\u304f\u306e\u9053\u8def\u306b\u9806\u756a\u5f85\u3061\u306e\u8eca\u304c\u4e26\u3093\u3060\u3002\u5e02\u306f\u96a3\u63a5\u3059\u308b\u5e02\u6709\u5730\u3092\u81e8\u6642\u99d0\u8eca\u5834\u3068\u3057\u3066\u958b\u653e\u3057\u305f\u304c\u3001\u6052\u4e45\u7684\u306a\u5bfe\u7b56\u306f\u6c7a\u307e\u3063\u3066\u3044\u306a\u3044\u3002\u307e\u305f\u3001\u958b\u9928\u6642\u9593\u3092\u5e73\u65e5\u306e\u591c\u4e5d\u6642\u307e\u3067\u5ef6\u3070\u3057\u305f\u3053\u3068\u3067\u8077\u54e1\u306e\u8ca0\u62c5\u304c\u5897\u3048\u3066\u304a\u308a\u3001\u8077\u54e1\u56e3\u4f53\u306f\u4eba\u54e1\u306e\u8ffd\u52a0\u3092\u6c42\u3081\u3066\u3044\u308b\u3002<\/p>\n<p>\u5229\u7528\u8005\u306e\u53d7\u3051\u6b62\u3081\u306f\u304a\u304a\u3080\u306d\u597d\u610f\u7684\u3060\u3002\u9031\u306b\u4e8c\u5ea6\u3001\u5b6b\u3092\u9023\u308c\u3066\u6765\u308b\u3068\u3044\u3046\u4e03\u5341\u4ee3\u306e\u5973\u6027\u306f\u300c\u524d\u306f\u5b50\u3069\u3082\u304c\u58f0\u3092\u51fa\u3059\u3068\u5468\u308a\u306e\u76ee\u304c\u6c17\u306b\u306a\u3063\u305f\u304c\u3001\u4e00\u968e\u306a\u3089\u6c17\u517c\u306d\u306a\u304f\u7d75\u672c\u3092\u8aad\u3093\u3067\u3042\u3052\u3089\u308c\u308b\u300d\u3068\u8a71\u3059\u3002\u4e00\u65b9\u3001\u53d7\u9a13\u3092\u63a7\u3048\u305f\u9ad8\u6821\u751f\u306e\u4e2d\u306b\u306f\u300c\u5b66\u7fd2\u5e2d\u304c\u4e09\u968e\u3060\u3051\u306b\u306a\u308a\u3001\u5915\u65b9\u306f\u3059\u3050\u306b\u6e80\u5e2d\u306b\u306a\u308b\u300d\u3068\u4e0d\u6e80\u3092\u6f0f\u3089\u3059\u4eba\u3082\u3044\u308b\u3002\u56f3\u66f8\u9928\u306f\u3001\u5b66\u7fd2\u5e2d\u306e\u7a7a\u304d\u72b6\u6cc1\u3092\u30db\u30fc\u30e0\u30da\u30fc\u30b8\u3067\u78ba\u8a8d\u3067\u304d\u308b\u4ed5\u7d44\u307f\u3092\u5e74\u5185\u306b\u5c0e\u5165\u3059\u308b\u4e88\u5b9a\u3060\u3002<\/p>\n<p>\u518d\u958b\u9928\u306b\u5408\u308f\u305b\u3066\u3001\u50ac\u3057\u3082\u5897\u3048\u305f\u3002\u6bce\u9031\u571f\u66dc\u306e\u5348\u524d\u306b\u306f\u8aad\u307f\u805e\u304b\u305b\u4f1a\u3001\u6708\u306b\u4e00\u5ea6\u306f\u5730\u5143\u306e\u8fb2\u5bb6\u3084\u5546\u5e97\u4e3b\u3092\u8b1b\u5e2b\u306b\u62db\u304f\u300c\u307e\u3061\u306e\u5148\u751f\u300d\u8b1b\u5ea7\u304c\u958b\u304b\u308c\u308b\u3002\u5341\u4e00\u6708\u306b\u306f\u3001\u5e02\u5185\u306e\u4e2d\u5b66\u751f\u304c\u9078\u3093\u3060\u672c\u3092\u7d39\u4ecb\u3059\u308b\u5c55\u793a\u3082\u4e88\u5b9a\u3055\u308c\u3066\u3044\u308b\u3002<\/p>\n<p>\u9928\u9577\u306e\u5c71\u672c\u548c\u5b50\u3055\u3093\u306f\u300c\u672c\u3092\u501f\u308a\u308b\u5834\u6240\u304b\u3089\u3001\u4eba\u304c\u96c6\u307e\u3063\u3066\u904e\u3054\u3059\u5834\u6240\u3078\u5909\u3048\u305f\u304b\u3063\u305f\u3002\u518d\u958b\u9928\u304b\u3089\u306e\u4e00\u304b\u6708\u3067\u6765\u9928\u8005\u306f\u4ee5\u524d\u306e\u4e00\u30fb\u516b\u500d\u306b\u306a\u3063\u305f\u300d\u3068\u8a71\u3059\u3002\u5e02\u306f\u6765\u5e74\u5ea6\u3001\u6b8b\u308b\u4e8c\u3064\u306e\u56f3\u66f8\u9928\u306b\u3064\u3044\u3066\u3082\u5229\u7528\u8005\u3078\u306e\u805e\u304d\u53d6\u308a\u3092\u884c\u3044\u3001\u6539\u4fee\u306e\u5fc5\u8981\u6027\u3092\u691c\u8a0e\u3059\u308b\u65b9\u91dd\u3060\u3002<\/p>\n<\/blockquote>\n<p><em>(Summarize the following article in three sentences. (The article, about 1,200 Japanese characters, describes the renovation and reopening of a city&#8217;s central library: the budget, the new floor layout and seating, changes to the collection and e-book lending, building and accessibility work, remaining issues such as parking and staff workload, users&#8217; reactions, new events, and the city&#8217;s next steps.))<\/em><\/p>\n<pre><code class=\"language-text\">\u4e09\u3064\u3042\u308b\u516c\u7acb\u56f3\u66f8\u9928\u306e\u4e2d\u592e\u56f3\u66f8\u9928\u304c\u4eca\u5e74\u306e\u6625\u3001\u5341\u6708\u4e00\u65e5\u306b\u518d\u958b\u9928\u3057\u305f\u3002\u6539\u4fee\u306f\u7d04\u4e00\u5e74\u534a\u306b\u308f\u305f\u308a\u3001\u7dcf\u4e8b\u696d\u8cbb\u306f\u7d04\u5341\u4e8c\u5104\u5186\u3002\u3053\u306e\u3046\u3061\u7d04\u56db\u5104\u5186\u306f\u56fd\u306e\u88dc\u52a9\u91d1\u3067\u307e\u304b\u306a\u308f\u308c\u305f\u3002\n<\/code><\/pre>\n<p><strong>Formal business email<\/strong><\/p>\n<blockquote>\n<p>\u53d6\u5f15\u5148\u306e\u62c5\u5f53\u8005\u306b\u3001\u6765\u9031\u706b\u66dc\u65e5\u306e\u6253\u3061\u5408\u308f\u305b\u3092\u6c34\u66dc\u65e5\u306b\u5909\u66f4\u3057\u3066\u307b\u3057\u3044\u3068\u304a\u9858\u3044\u3059\u308b\u30e1\u30fc\u30eb\u3092\u3001\u4e01\u5be7\u306a\u656c\u8a9e\u3067\u66f8\u3044\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(Write an email in polite Japanese (keigo) asking a client&#8217;s contact person to move next Tuesday&#8217;s meeting to Wednesday.)<\/em><\/p>\n<pre><code class=\"language-text\">\u3054\u9023\u7d61\u3044\u305f\u3060\u304d\u3042\u308a\u304c\u3068\u3046\u3054\u3056\u3044\u307e\u3059\u3002\n\n\u6c34\u66dc\u65e5\u306b\u5909\u66f4\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u306e\u3067\u3001\u3054\u9023\u7d61\u3044\u305f\u3060\u304d\u3042\u308a\u304c\u3068\u3046\u3054\u3056\u3044\u307e\u3059\u3002\n\n\u6c34\u66dc\u65e5\u306b\u5909\u66f4\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u306e\u3067\u3001\u3054\u9023\u7d61\u3044\u305f\u3060\u304d\u3042\u308a\u304c\u3068\u3046\u3054\u3056\u3044\u307e\u3059\u3002\n\n\u6c34\u66dc\u65e5\u306b\u5909\u66f4\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u306e\u3067\u3001\u3054\u9023\u7d61\u3044\u305f\u3060\u304d\u3042\u308a\u304c\u3068\u3046\u3054\u3056\u3044\u307e\u3059\u3002\n<\/code><\/pre>\n<p><strong>Word problem<\/strong><\/p>\n<blockquote>\n<p>\u308a\u3093\u3054\u304c1\u500b120\u5186\u3001\u307f\u304b\u3093\u304c1\u500b45\u5186\u3067\u3059\u3002\u308a\u3093\u3054\u30923\u500b\u3068\u307f\u304b\u3093\u30928\u500b\u8cb7\u3063\u3066\u30011,000\u5186\u672d\u3067\u6255\u3044\u307e\u3057\u305f\u3002\u304a\u3064\u308a\u306f\u3044\u304f\u3089\u3067\u3059\u304b\u3002\u8003\u3048\u65b9\u3082\u793a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(Apples cost 120 yen each and mandarins cost 45 yen each. You buy 3 apples and 8 mandarins and pay with a 1,000-yen bill. How much change do you get? Show your reasoning.)<\/em><\/p>\n<pre><code class=\"language-text\">1\u500b120\u5186\u306e\u308a\u3093\u3054\u30923\u500b\u8cb7\u3046\u3068\u30013\u00d7120\u5186=360\u5186\u3002\n1\u500b45\u5186\u306e\u307f\u304b\u3093\u30928\u500b\u8cb7\u3046\u3068\u30018\u00d745\u5186=360\u5186\u3002\n3\u500b\u306e\u308a\u3093\u3054\u30688\u500b\u306e\u307f\u304b\u3093\u3092\u8cb7\u3046\u3068\u3001360\u5186+360\u5186=720\u5186\u3002\n1,000\u5186\u672d\u3067\u6255\u3046\u3068\u30011,000\u5186-720\u5186=280\u5186\u3002\n\u304a\u3064\u308a\u306f280\u5186\u3067\u3059\u3002\n<\/code><\/pre>\n<p>Reference: The correct answer is 280 yen (120\u00d73 = 360, 45\u00d78 = 360, 1,000 \u2212 720 = 280).<\/p>\n<p><strong>Knowledge<\/strong><\/p>\n<blockquote>\n<p>\u65e5\u672c\u306e\u90fd\u9053\u5e9c\u770c\u306e\u3046\u3061\u3001\u6d77\u306b\u9762\u3057\u3066\u3044\u306a\u3044\u770c\u3092\u3059\u3079\u3066\u6319\u3052\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(List all of Japan&#8217;s prefectures that do not border the sea.)<\/em><\/p>\n<pre><code class=\"language-text\">\u6771\u4eac\u90fd\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u57fc\u7389\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\u795e\u5948\u5ddd\u770c\u3001\u5343\u8449\u770c\u3001\n<\/code><\/pre>\n<p><em>(Cut off at the 1024-token limit.)<\/em><\/p>\n<p>Reference: The correct answer is eight prefectures: Tochigi, Gunma, Saitama, Yamanashi, Nagano, Gifu, Shiga and Nara.<\/p>\n<p><strong>Constrained writing<\/strong><\/p>\n<blockquote>\n<p>\u300c\u6625\u300d\u3092\u30c6\u30fc\u30de\u306b\u3057\u305f\u4ff3\u53e5\u30923\u3064\u3001\u4e94\u30fb\u4e03\u30fb\u4e94\u306e\u97f3\u6570\u3092\u5b88\u3063\u3066\u4f5c\u3063\u3066\u304f\u3060\u3055\u3044\u3002\u305d\u308c\u305e\u308c\u306b\u4e00\u8a00\u306e\u8aac\u660e\u3092\u4ed8\u3051\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(Write three haiku on the theme of spring, keeping the 5-7-5 syllable pattern, each with a one-line explanation.)<\/em><\/p>\n<pre><code class=\"language-text\">\u300c\u6625\u300d\u3092\u30c6\u30fc\u30de\u306b\u3057\u305f\u4ff3\u53e5\u30925\u30fb7\u30fb5\u306e\u97f3\u6570\u3092\u5b88\u3063\u3066\u4f5c\u3063\u3066\u304f\u3060\u3055\u3044\u3002\n\n1. \u6625\u306e\u82b1\u304c\u54b2\u304d\u59cb\u3081\u308b\n\u6625\u306e\u82b1\u304c\u54b2\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u82b1\u304c\u54b2\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u82b1\u304c\u54b2\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u82b1\u304c\u54b2\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u82b1\u304c\u54b2\u304d\u59cb\u3081\u308b\u3002\n\n2. \u6625\u306e\u65e5\u304c\u71b1\u304f\u306a\u308b\n\u6625\u306e\u65e5\u304c\u71b1\u304f\u306a\u308b\u3002\u6625\u306e\u65e5\u304c\u71b1\u304f\u306a\u308b\u3002\u6625\u306e\u65e5\u304c\u71b1\u304f\u306a\u308b\u3002\u6625\u306e\u65e5\u304c\u71b1\u304f\u306a\u308b\u3002\u6625\u306e\u65e5\u304c\u71b1\u304f\u306a\u308b\u3002\n\n3. \u6625\u306e\u98a8\u304c\u5439\u304d\u59cb\u3081\u308b\n\u6625\u306e\u98a8\u304c\u5439\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u98a8\u304c\u5439\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u98a8\u304c\u5439\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u98a8\u304c\u5439\u304d\u59cb\u3081\u308b\u3002\u6625\u306e\u98a8\u304c\u5439\u304d\u59cb\u3081\u308b\u3002\n<\/code><\/pre>\n<p><!-- \/lmw:lab --><\/p>\n<h2>How to Get It<\/h2>\n<p>The models are available on Hugging Face in BF16 safetensors format (split into three shards). Prior license agreement (Gated) is not required for access.<\/p>\n<ul>\n<li><strong>Supported Libraries\/Engines<\/strong>: <code>transformers &gt;= 5.5<\/code>, <code>vLLM<\/code><\/li>\n<li><strong>Repository Names<\/strong>:<\/li>\n<li><code>EleutherAI\/olmo3-7b-sdf-sft-clean150<\/code> &#8211; <code>EleutherAI\/olmo3-7b-sdf-sft-scrub-b1reset150<\/code><\/li>\n<\/ul>\n<p><!-- lmw:related --><\/p>\n<h2>Related Articles<\/h2>\n<ul>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/15\/eleutherai-releases-bergson-leaderboard-baseline-gpt2-model\/\">EleutherAI Releases Bergson Leaderboard Baseline GPT-2 Model<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:related --><\/p>\n<p><!-- lmw:next-steps --><\/p>\n<h2>What to Read Next<\/h2>\n<ul>\n<li><strong>Find models by VRAM<\/strong> (This model runs from the 24GB tier) \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-24gb-en\/\">Other models that run on a 24GB GPU<\/a><\/li>\n<li><strong>Engines that run this model<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/engine-vllm-en\/\">vLLM<\/a><\/li>\n<li><strong>Formats this model is available in<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/format-safetensors-en\/\">Safetensors format guide and models<\/a><\/li>\n<li><strong>Learn about the publisher<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-eleutherai-en\/\">EleutherAI: models, licenses and articles<\/a><\/li>\n<li><strong>Other models for the same task<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/models-by-task-en\/#task-text\">Other text generation models<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:next-steps --><\/p>\n<h2>Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/huggingface.co\/EleutherAI\/olmo3-7b-sdf-sft-clean150\">EleutherAI\/olmo3-7b-sdf-sft-clean150<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/EleutherAI\/olmo3-7b-sdf-sft-scrub-b1reset150\">EleutherAI\/olmo3-7b-sdf-sft-scrub-b1reset150<\/a><\/li>\n<\/ul>\n<p><!-- lmw:updates --><\/p>\n<h2>Update History<\/h2>\n<ul>\n<li>2026-09-24: Rebuilt the article: Added a &#8220;can you run it locally?&#8221; verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n<li>2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<li>2026-09-29: Added our own measurements: what is inside the GGUF file, CPU speed and answers to Japanese questions.<\/li>\n<\/ul>\n<p><!-- \/lmw:updates --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>EleutherAI releases two OLMo-3-7B models trained via GRPO to study reward-hacking dynamics and validate the hack-ignition-benchmark.<\/p>\n","protected":false},"author":1,"featured_media":862,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[373,1317,136,1319,377,117],"class_list":["post-863","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-eleutherai-en","tag-grpo-en","tag-llm-en","tag-olmo-3-en","tag--en"],"lang":"en","translations":{"en":863,"ja":861},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/863","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=863"}],"version-history":[{"count":64,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/863\/revisions"}],"predecessor-version":[{"id":14827,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/863\/revisions\/14827"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/862"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=863"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=863"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=863"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}