Researchers propose TGRL (Temperature-Grouped Reinforcement Learning), a method that groups rollouts by temperature to improve exploration efficiency in reinforcement learning with verifiable rewards (RLVR) for large language models. The approach targets a central bottleneck in RLVR, where temperature control and test-time scaling strategies either expand the sample budget at rollout time or leave exploration constrained.
Efficient exploration often remains a central bottleneck in reinforcement learning with verifiable rewards (RLVR). Although temperature control and test-time scaling strategies can increase rollout diversity of large language models (LLMs), they either expand the sample budget at rollout time or lea
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