VGF solves behavior-regularized RL by transporting particles from a reference distribution to the value-induced optimal policy via discrete value-guided gradient flow.
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
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PCBF learns return distributions via source-consistent Bellman-coupled paths with shared noise and λ-parameterized control variates, reporting improved fidelity and stability on MRPs, OGBench, and D4RL.
Flow matching critics outperform monolithic ones in RL by 2x performance and 5x sample efficiency via test-time error recovery through integration and multi-point velocity supervision that preserves feature plasticity.
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Reinforcement Learning via Value Gradient Flow
VGF solves behavior-regularized RL by transporting particles from a reference distribution to the value-induced optimal policy via discrete value-guided gradient flow.
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Path-Coupled Bellman Flows for Distributional Reinforcement Learning
PCBF learns return distributions via source-consistent Bellman-coupled paths with shared noise and λ-parameterized control variates, reporting improved fidelity and stability on MRPs, OGBench, and D4RL.