两篇新的研究论文介绍了用于增强语言模型推理的新型强化学习技术。第一篇 GAGPO 提出了一种无批评者的方法，用于在多轮环境中进行精确的时间信用分配，从而改进了与步骤对齐的学习。第二篇 CoDistill-GRPO 提出了一种联合蒸馏方法，用于同时训练大型和小型语言模型，使得分组相对策略优化对于小型模型来说更高效、更易于使用。 AI
影响 这些论文介绍了新的强化学习技术，可以提高大型语言模型的推理能力和训练效率。
排序理由 两篇介绍用于语言模型的新型强化学习算法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge. Agents often receive sparse, trajectory-level rewards only at the end of an episode, making it difficult to d…
Group Relative Policy Optimization (GRPO) has emerged as a powerful algorithm for improving the reasoning capabilities of language models, but often fails to improve small models due to sparse rewards on difficult tasks. Existing works mitigate this issue by leveraging a larger m…