# Temperature-Grouped Reinforcement Learning

The work proposes Temperature-Grouped Reinforcement Learning (TGRL), which partitions each prompt's rollout group into low- and high-temperature subsets, estimates prompt-level exploration gain from their reward contrast, and allocates that gain as token-level credit using Jensen–Shannon divergence between the temperature-scaled next-token distributions induced by the same logits; across 11 benchmarks, TGRL improves the six-benchmark math average by 1.6% at 32B, raises CodeForces rating by 196.7 points and LiveCodeBench Pass@16 by 4.4%, and improves ALFWorld/WebShop success rates by 6.3%/4.9%, reaching equivalent accuracy up to 36% faster than strong RLVR baselines without expanding the rollout budget.

The work proposes Temperature-Grouped Reinforcement Learning (TGRL), which partitions each prompt's rollout group into low- and high-temperature subsets, estimates prompt-level exploration gain from their reward contrast, and allocates that gain as token-level credit using Jensen–Shannon divergence between the temperature-scaled next-token distributions induced by the same logits; across 11 benchmarks, TGRL improves the six-benchmark math average by 1.6% at 32B, raises CodeForces rating by 196.7 points and LiveCodeBench Pass@16 by 4.4%, and improves ALFWorld/WebShop success rates by 6.3%/4.9%, reaching equivalent accuracy up to 36% faster than strong RLVR baselines without expanding the rollout budget.