142 upvotes · 17 AUG 2026 · Yixuan Wang, Yifei Chen, Haichao Zhang et al.
This paper improves reinforcement learning for post-training language model reasoners by addressing two problems in existing methods: identical advantages for distinct reward profiles and fixed relative weights for all objectives. A new method, SA-MRPO, dynamically reallocates optimization effort toward under-optimized objectives while maintaining performance on well-satisfied objectives.
78 upvotes · 8 SEP 2026 · Youngrok Park, Sangmin Bae, Hojung Jung et al.
This paper introduces a method called On-Policy Reverse Distillation that helps stronger models learn from weaker supervisors by selectively amplifying the parts of the weaker model's guidance that are most useful to the stronger model. This can lead to faster and more efficient learning, especially in situations where it's expensive to retrain models from scratch.
73 upvotes · 28 JUL 2026 · Bo-Wen Zhang, Junwei He, Wen Wang et al.
This paper proposes a method to improve language model training by allocating credit to individual tokens within a response, allowing for more nuanced evaluation of model performance. Practitioners may care about this method as it can lead to better language model performance, especially in tasks that require specific formatting or semantic choices.