LLM News

A new research paper proposes Rewards as Labels (REAL), a framework that reframes reinforcement learning with verifiable rewards as a classification problem rather than scalar weighting. The method addresses fundamental gradient optimization issues in current GRPO variants and demonstrates measurable improvements on mathematical reasoning benchmarks.

A new paper identifies a fundamental flaw in standard reinforcement learning fine-tuning approaches for large language models: the choice of divergence term directly causes the degradation of multi-attempt performance (Pass@k) despite single-attempt improvements. Researchers propose Diversity-Preserving Hybrid RL (DPH-RL), which uses mass-covering f-divergences to maintain broad solution coverage and prevent catastrophic forgetting.