65 lines
2.4 KiB
Markdown
65 lines
2.4 KiB
Markdown
---
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base_model:
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- Qwen/Qwen3-8B-Base
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datasets:
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- Elliott/Openr1-Math-46k-8192
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language:
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- en
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license: apache-2.0
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metrics:
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- accuracy
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pipeline_tag: text-generation
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library_name: transformers
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---
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[](https://arxiv.org/abs/2510.26109)
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[](https://github.com/JamyDon/LTE)
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[](LICENSE)
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## Introduction
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LTE is an RLVR approach that mitigates the exploration stagnation of LMs by their previously self-made mistakes and does not require any external expert guidance. LTE improves the performance upper bound of LMs and enhances both exploitation and exploration during training.
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## Key Highlights
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- **Self-generated Hints**: LTE uses the errors generated by the LMs themselves during training as hints.
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- **No External Expert Guidance**: LTE does not require any external expert guidance to mitigate the exploration stagnation of LMs.
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## Inference
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Here is an example of using LTE models for inference:
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_path="JamyDohrn/LTE-Qwen3-8B-Base"
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question = "which number is larger? 9.11 or 9.9?"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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messages = [{"role": "user", "content": question}]
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chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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llm = LLM(model=model_path)
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params = SamplingParams(temperature=0.6, max_tokens=32768)
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outputs = llm.generate([chat], params)
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print(outputs[0].outputs[0].text)
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```
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## Acknowledgements
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LTE is built on the following repositories and we thank their teams for their valuable contributions to the community:
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- [verl](https://github.com/volcengine/verl)
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- [LUFFY](https://github.com/ElliottYan/LUFFY)
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- [LIMO](https://github.com/GAIR-NLP/LIMO)
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## Citation
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If you find our work useful, feel free to cite our paper:
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```bib
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@misc{tang2026steprivertwicelearning,
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title={Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error},
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author={Chenming Tang and Hsiu-Yuan Huang and Weijie Liu and Clive Bai and Saiyong Yang and Yunfang Wu},
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year={2026},
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eprint={2510.26109},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2510.26109},
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}
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``` |