将强化学习与可验证奖励结合，让大模型在多买家市场中学会策略性谈判，博弈论新玩法。
arXiv:2607.05863v1 Announce Type: new Abstract: Negotiation is a fundamental strategic interaction in management science, characterized by agents atte…
揭示RLVR训练中参数轨迹的秩一结构，仅需极小规模训练即可外推LLM推理能力，颠覆传统认知。
arXiv:2605.21468v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving rea…
探索强化学习与可验证奖励在知识密集型领域对LLM推理能力的提升，填补研究空白。
arXiv:2605.18261v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance …
提出RLBFF，用二元灵活反馈桥接人类偏好与可验证奖励，提升大模型对齐效率。
arXiv:2509.21319v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with Verifiable…
突破现有强化学习局限，提出通过主动交互提升大模型推理能力的新方法。
arXiv:2605.08401v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have demonstrated remarkable reasoning capab…
用随机少样本指导提升RLVR在困难问题上的样本效率，大模型训练新思路。
arXiv:2605.15012v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved great success in developing Large…