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--- |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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library_name: peft |
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license: mit |
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datasets: |
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- Roihn/Einstein-Puzzles-Data |
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language: |
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- en |
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--- |
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# Einstein-Puzzles |
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**Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry** ([Arxiv](https://arxiv.org/abs/2510.25595)) |
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*Run Peng\*, Ziqiao Ma\*, Amy Pang, Sikai Li, Zhang Xi-Jia, Yingzhuo Yu, Cristian-Paul Bara, Joyce Chai* |
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## Model Details |
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For all the model fine-tuning, we employ LoRA with a rank of 32, training with a global batch size of 128 and a learning rate of 2e-4 using a cosine decay schedule for 1 epoch. Fine-tuning is conducted using [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF), while FlashAttention-2 is used to speed up training. The process takes approximately 30 minutes on 4 A40 GPUs with 48GB RAM each. |
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This repo provides the fine-tuned model with full capability of information providing and seeking and chain-of-thought reasoning. |
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## Citation |
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```bibtex |
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@misc{peng2025communicationverificationllmagents, |
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title={Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry}, |
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author={Run Peng and Ziqiao Ma and Amy Pang and Sikai Li and Zhang Xi-Jia and Yingzhuo Yu and Cristian-Paul Bara and Joyce Chai}, |
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year={2025}, |
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eprint={2510.25595}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2510.25595}, |
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} |
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``` |
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