Multi-reward reinforcement learning steadies language model training with two signals
Photo: The Cryptonomist
A paper by Shourov Joarder and three other authors proposes multi-reward reinforcement learning to make large language model training more stable without external supervision. The authors report that combining two internal reward signals improves robustness on mathematical reasoning and code-generation benchmarks, with performance approaching that of supervised methods. The approach builds on reinforcement learning from internal feedback (RLIF) , which draws training signals from the model itself. That differs from reinforcement learning with verifiable rewards (RLVR), which often relies on human annotations or gold-standard solutions.
Source: The Cryptonomist
