新闻
OR Else: A Differentiable Trust Region for Policy Optimization
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers propose Output Reset, a smooth alternative to clipped objectives in PPO and GRPO for language model training, replacing abrupt gradient changes with squared-margin loss.
- 为何重要
- Matters for engineers optimizing reinforcement learning training stability in large language models, particularly when fine-tuning with policy gradient methods.
- 注意
- Results are mixed: PPO-OR shows gains under GAE but GRPO-OR does not improve reward scores at group size two. Scores measure training-time reward models, not held-out human preferences.
- language model
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