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.