Author

Chris Kong

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#artificial intelligence Review Sep 2026

MInTRL: Off-policy Intervention can boost On-policy RL

This work introduces Minimal Intervention Reinforcement Learning (MInTRL), which expands the exploration frontier through sparse, local interventions in otherwise on-policy rollouts, and establishes minimal intervention as an effective paradigm for enhancing on-policy RL.

Ming-Yu Chen, Ye-Fan Tao, Gerald Friedland et al. · 0 citations

#machine learning Preprint Sep 2026

Cliff: Learning Process Rewards from the First Mistake

Cliff, a reward shaping strategy that utilizes an off-the-shelf LLM as a teacher to identify the first mistake in each rollout, is proposed and established as a simple, general and effective approach for improving RLVR with richer, fine-grained supervision.

Pei-Xuan Han, Runnan Wang, Ketan Ramaneti et al. · 1 citation