llm-jp-4.1-thinking

LLM-jp-4.1 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics.

This repository provides the llm-jp-4.1-8b-thinking model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to:

• LLM-jp-4.1 Models

Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.

For more details on the training procedures and evaluation results, please refer to our technical blog (in Japanese).

For practical usage examples and detailed instructions on how to use the models, please also refer to our cookbook.

To support the continued development of LLM-jp, we would greatly appreciate it if you could share how you utilize LLM-jp outcomes via the survey form.

Usage

Please refer to our cookbook for practical usage examples and detailed instructions on how to use the models.

Model Details

• Model type: Transformer-based Language Model
• Architectures:

Dense model:

MoE model:

Tokenizer

The tokenizer of this model is based on a Unigram byte-fallback model implemented with huggingface/tokenizers. The vocabulary entries were converted from llm-jp-tokenizer v4.0. Please refer to README.md of llm-jp-tokenizer for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).

[!NOTE] The chat template of this model is designed to be compatible with the OpenAI Harmony response format. However, the tokenizer differs from the one assumed by the openai-harmony library, and therefore direct tokenization with openai-harmony is not supported. For correct behavior, please use the tokenizer provided with this model. For detailed usage, please refer to our cookbook.

Training

Pre-training

This model was trained through a multi-stage pipeline consisting of pre-training and mid-training phases, using a total of 11.7T tokens.

The corpora used for pre-training and mid-training are publicly available at the following links:

• Pre-training
• Mid-training

[!NOTE] Although most of the corpora have been released, some portions are excluded from public release due to licensing constraints.

Post-training

We have fine-tuned the pre-trained checkpoint using SFT and further aligned it with DPO.

The datasets used for post-training are also publicly available at the following links:

• SFT
• DPO (for llm-jp-4.1-8b-thinking model)
• DPO (for llm-jp-4.1-32b-a3b-thinking model)
• DPO (for llm-jp-4.1-33b-thinking model)

Evaluation

We evaluated llm-jp-4.1 on a variety of benchmarks covering general capabilities, safety, and tool calling.

For more detailed evaluation results and analysis, please refer to our technical blog.

swallow-evaluation-instruct

We evaluated the models on a range of benchmarks covering the following six categories:

• Math
  • Math 500
  • AIME 2024 (pass@1, pass@32)
  • AIME 2025 (pass@1, pass@32)
  • AIME 2026 (pass@1, pass@32)
  • MCLM Math 100 (pass@1, pass@4)
  • PolyMath JA High
  • PolyMath JA Top
• Science
  • GPQA Diamond (pass@1, pass@4)
  • JGPQA Diamond
• Knowledge & QA
  • JAM-CQA
  • JEMHopQA
  • JMMLU
  • MMLU-ProX JA
  • MMLU-ProX EN
• Code
  • LiveCodeBench v6 (pass@1, pass@10)
  • JHumanEval (pass@1, pass@10)
  • HumanEval+ (pass@1, pass@10)
• Instruction Following (IF)
  • MIFEval JA
  • IFBench
• Machine Translation (MT)
  • WMT20 EN-JA
  • WMT20 JA-EN

For LLM-jp and gpt-oss models, reasoning_effort was set to high. For Olmo-3-7B-Think, Olmo-3.1-32B-Think, Qwen3, Qwen3.5, Qwen3.6, and Gemma 4, enable_thinking was set to True. For Qwen3.8-27B and Muse-Glimmer-30B, reasoning_effort was set to xhigh.

The figure below shows the average score across the benchmarks in each category.

llm-jp-judge

We evaluated the models using an LLM-as-a-Judge framework on the following benchmarks:

• MT-Bench (JA/EN): A benchmark for measuring multi-turn conversational task-solving ability.
• AnswerCarefully: A benchmark for evaluating safety in Japanese. We used 3