Researchers Propose Two-Reward Method for More Stable AI Training

Shourov Joarder and three co-authors propose a way to train language models with two forms of self-generated feedback, aiming to make reinforcement learning more stable without external supervision. The approach uses reinforcement learning from internal feedback (RLIF), in which a model generates its own training signals. By comparison, reinforcement learning with verifiable rewards (RLVR) often uses human annotations or gold-standard solutions. The authors say RLIF methods that rely on a single reward can be vulnerable to reward hacking, entropy collapse and weakened reasoning structure.