| --- |
| license: mit |
| --- |
| <div align="center"> |
| <h1 align="center"> 👉 LightThinker 👈 </h1> |
| <b>LightThinker Family: From Thinking Compression to Adaptive Memory Management</b> |
|
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| [](https://github.com/zjunlp/LightThinker) |
| [](https://opensource.org/licenses/MIT) |
|  |
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| <p align="center"> |
| <a href="https://github.com/zjunlp/lightthinker">Github</a> • |
| <a href="https://arxiv.org/abs/2502.15589">📄 LightThinker arXiv</a> • |
| <a href="http://arxiv.org/abs/2604.03679">📄 LightThinker++ arXiv</a> • |
| <a href="https://huggingface.co/collections/zjunlp/lightthinker-67f9faaaa518f2e00b17386b">🤗 Hugging Face</a> |
| </p> |
| </div> |
|
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|
|
| ## Table of Contents |
|
|
| - 🔔 [News](#news) |
| - 👀 [Overview](#overview) |
| - 🤗 [Resources](#resources) |
| - 📁 [Repository Structure](#repository-structure) |
| - 🚀 [LightThinker++ — General Reasoning](#lightthinker--general-reasoning) |
| - 🌐 [LightThinker++ — Agentic Reasoning](#lightthinker--agentic-reasoning) |
| - 💡 [LightThinker](#lightthinker) |
| - 🎁 [Acknowledgement](#acknowledgement) |
| - 🚩 [Citation](#citation) |
|
|
| ## 🔔 News |
|
|
| - **[2026-03]** We release a new paper: "[LightThinker++: From Reasoning Compression to Memory Management](http://arxiv.org/abs/2604.03679)". |
| - **[2025-08]** Our paper "[LightThinker: Thinking Step-by-Step Compression](https://arxiv.org/abs/2502.15589)" has been accepted to EMNLP 2025. |
| - **[2025-02]** We release "[LightThinker: Thinking Step-by-Step Compression](https://arxiv.org/abs/2502.15589)". |
|
|
| ## 👀 Overview |
|
|
| LLMs can solve increasingly complex reasoning tasks, but long thought traces make inference expensive because the model must retain and attend to large contexts. The **LightThinker family** addresses this by compressing or actively managing intermediate reasoning states. |
|
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| - **LightThinker** compresses intermediate thoughts into compact gist-token representations. The implementation and AnLLM baseline are preserved under [`lightthinker_v1/`](./lightthinker_v1/). |
| - **LightThinker++** introduces explicit adaptive memory management. The model can use memory primitives such as `commit`, `expand`, `fold`, and `final_answer` to control its scratchpad while reasoning. |
|
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| This top-level README is a quick-start guide. Detailed model links, data links, environment variables, script parameters, and evaluation options live in the module README files. |
|
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| ## 🤗 Resources |
|
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| - **LightThinker++ General Reasoning:** models, SFT data, and full configuration are in [`general_reasoning/README.md`](./general_reasoning/README.md). |
| - **LightThinker++ Agentic Reasoning:** models, SFT data, and full configuration are in [`agentic_reasoning/README.md`](./agentic_reasoning/README.md). |
| - **LightThinker:** models, data archive, and full configuration are in [`lightthinker_v1/README.md`](./lightthinker_v1/README.md). |
|
|
| ## 📁 Repository Structure |
|
|
| ``` |
| LightThinker/ |
| ├── general_reasoning/ # LightThinker++ for math and general reasoning tasks |
| ├── agentic_reasoning/ # LightThinker++ for agentic deep-research tasks |
| ├── lightthinker_v1/ # LightThinker and AnLLM baseline code |
| ├── assets/ # README assets |
| └── README.md |
| ``` |
|
|
| | Method | Folder | Full guide | |
| |--------|--------|------------| |
| | LightThinker++ for general reasoning | [`general_reasoning/`](./general_reasoning/) | [`general_reasoning/README.md`](./general_reasoning/README.md) | |
| | LightThinker++ for agentic reasoning | [`agentic_reasoning/`](./agentic_reasoning/) | [`agentic_reasoning/README.md`](./agentic_reasoning/README.md) | |
| | LightThinker | [`lightthinker_v1/`](./lightthinker_v1/) | [`lightthinker_v1/README.md`](./lightthinker_v1/README.md) | |
|
|
| ## 🚀 LightThinker++ — General Reasoning |
|
|
| Quick start for math and general reasoning tasks such as GSM8K, MMLU, GPQA, and BBH: |
|
|
| ```bash |
| cd general_reasoning |
| conda env create -f environment.yml |
| conda activate lt_plus_general_reasoning |
| cp .env.example ../.env |
| |
| # Generate synthetic trajectories after setting an input JSONL dataset. |
| DATASET_PATH=/path/to/input.jsonl bash scripts/synthetic.sh |
| |
| # Run inference with a trained or downloaded model. |
| MODEL=/path/to/model bash scripts/infer.sh 0 gsm8k |
| ``` |
|
|
| See [`general_reasoning/README.md`](./general_reasoning/README.md) for configuration, datasets, and full arguments. |
|
|
| ## 🌐 LightThinker++ — Agentic Reasoning |
|
|
| Quick start for long-horizon deep-research and multi-hop QA tasks: |
|
|
| ```bash |
| cd agentic_reasoning |
| bash setup.sh |
| cp .env.synthetic.example .env |
| |
| # Run synthetic data generation with the included sample dataset. |
| bash scripts/synthetic.sh |
| ``` |
|
|
| For inference, use `cp .env.infer.example .env`, configure your model endpoint, and run `bash scripts/inference.sh`. |
|
|
| See [`agentic_reasoning/README.md`](./agentic_reasoning/README.md) for configuration, datasets, and full arguments. |
|
|
| ## 💡 LightThinker |
|
|
| Quick start for the LightThinker implementation: |
|
|
| ```bash |
| cd lightthinker_v1 |
| conda create -n lightthinker python=3.9 -y |
| conda activate lightthinker |
| pip install -r requirements.txt |
| |
| # Train and run inference. |
| bash train.sh |
| bash inference.sh |
| ``` |
|
|
| See [`lightthinker_v1/README.md`](./lightthinker_v1/README.md) for configuration, datasets, and full arguments. |
|
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| ## 🎁 Acknowledgement |
|
|
| We use the [`ms-swift`](https://github.com/modelscope/ms-swift) framework for full-parameter SFT of the LightThinker++ models. The general reasoning evaluation also builds on ideas from [TokenSkip](https://github.com/carriex/TokenSkip). The LightThinker evaluation includes baseline code inspired by H2O from [Meta-llama](https://github.com/meta-llama/llama-cookbook) and SepLLM from [HKUDS](https://github.com/HKUDS/SepLLM). |
|
|
| ## 🚩 Citation |
|
|
| If this work is helpful, please kindly cite: |
|
|
| ```bibtex |
| @article{lightthinker++, |
| author = {Yuqi Zhu and |
| Jintian Zhang and |
| Zhenjie Wan and |
| Yujie Luo and |
| Shuofei Qiao and |
| Zhengke Gui and |
| Da Zheng and |
| Lei Liang and |
| Huajun Chen and |
| Ningyu Zhang}, |
| title = {LightThinker++: From Reasoning Compression to Memory Management}, |
| journal = {CoRR}, |
| volume = {abs/2604.03679}, |
| year = {2026}, |
| url = {https://doi.org/10.48550/arXiv.2604.03679}, |
| doi = {10.48550/ARXIV.2604.03679}, |
| eprinttype = {arXiv}, |
| eprint = {2604.03679}, |
| timestamp = {Fri, 08 May 2026 07:40:46 +0200}, |
| biburl = {https://dblp.org/rec/journals/corr/abs-2604-03679.bib}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
| |
| @inproceedings{lightthinker, |
| author = {Jintian Zhang and |
| Yuqi Zhu and |
| Mengshu Sun and |
| Yujie Luo and |
| Shuofei Qiao and |
| Lun Du and |
| Da Zheng and |
| Huajun Chen and |
| Ningyu Zhang}, |
| editor = {Christos Christodoulopoulos and |
| Tanmoy Chakraborty and |
| Carolyn Rose and |
| Violet Peng}, |
| title = {LightThinker: Thinking Step-by-Step Compression}, |
| booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural |
| Language Processing, {EMNLP} 2025, Suzhou, China, November 4-9, 2025}, |
| pages = {13307--13328}, |
| publisher = {Association for Computational Linguistics}, |
| year = {2025}, |
| url = {https://doi.org/10.18653/v1/2025.emnlp-main.673}, |
| doi = {10.18653/V1/2025.EMNLP-MAIN.673}, |
| timestamp = {Mon, 02 Feb 2026 09:39:37 +0100}, |
| biburl = {https://dblp.org/rec/conf/emnlp/ZhangZSLQDZCZ25.bib}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
| ``` |
