#exploration2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago6

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Recycling Failures: Salvaging Exploration in RLVR via Fine-Grained Off-Policy Guidance

Researchers propose SCOPE, a new framework for Reinforcement Learning from Verifiable Rewards (RLVR) that improves AI reasoning by salvaging partially correct solutions rather than discarding them entirely. The method achieves 46.6% accuracy on math reasoning tasks and 53.4% on out-of-distribution problems by using step-wise correction to maintain exploration diversity.

AINeutralarXiv – CS AI · 4h ago0

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Adaptive Correlation-Weighted Intrinsic Rewards for Reinforcement Learning

Researchers propose ACWI, a new reinforcement learning framework that dynamically balances intrinsic and extrinsic rewards through adaptive scaling coefficients. The system uses a lightweight Beta Network to optimize exploration in sparse reward environments, demonstrating improved sample efficiency and stability in MiniGrid experiments.