Extreme Region Policy Distillation
Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-policy methods discard trajectories after a single update, while off-policy reuse introduces distribution mismatch that existing trust-region techniques mitigate primarily by enforcing conservative optimization, often leaving rich training signals underutilized. To investigate this, we perform extensive off-policy updates on fixed data. Our experiments reveal that aggressive multi-step optimization brings rapid initial gains, but excessive updates cause trajectory probabilities to deviate and entropy to collapse, with performance plateauing early...
| Authors | Changyu Chen, Xiting Wang, Rui Yan |
|---|---|
| Primary category | cs.LG |
| Published | 25 May 2026 |
| Last updated at source | 4 June 2026 |
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