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The paper introduces Trace, a credit-guided framework that compiles noisy interaction histories into executable walkthroughs to enhance long-horizon AI agent performance, showing significant improvements in experiments.
This paper compares prior injection methods for sparse-reward reinforcement learning in vision-language math reasoning, finding that effectiveness depends on delivery and that certain evaluation slices can mislead generalization assessments.
SCOUT introduces per-context reset curricula for sparse-reward reinforcement learning, adapting assistance removal to each context's learning progress, outperforming global pacing methods in navigation and manipulation tasks.
This paper investigates the use of the Muon optimizer in reinforcement learning post-training, finding that applying Muon to hidden weight matrices significantly improves success rates on ALFRED tasks compared to AdamW, with results dependent on the advantage estimator and learning rate.
Mesh-RL is a spatial domain-decomposition framework for reinforcement learning that partitions the environment into overlapping subgrids to accelerate temporal-difference learning and long-range credit assignment, improving convergence speed and sample efficiency in sparse-reward environments.
Proposes ULPS, a framework integrating a calibrated LLM into RL training with uncertainty-modulated guidance and A*-based symbolic trajectories, achieving improved success rate and sample efficiency on MiniGrid-UnlockPickup.