Decoupling Exploration from Optimization in RLVR
Presents novel training methodology for ML engineers working on reinforcement learning with language models, with practical code and checkpoints available.
AI Summary
Researchers propose Exploration-Distillation (ExpDis), a method that decouples exploration from optimization in RLVR , improving mathematical reasoning performance across seven benchmarks.
Excerpt
Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas absent from its prior training data. In practice, however, augmenting RLVR with strong novelty incentives has seen limited success and can degrade model quality. Because verifiable rewards supervise only a narrow slice of the model's knowledge and behavior, such