What Does Flow Matching Bring To TD Learning?
Recent work shows that flow matching can be effective for scalar Q-value function estimation in reinforcement learning (RL), but it remains unclear why or how this approach differs from standard critics. Contrary to conventional belief, we show that their success is not explained by distributional RL, as explicitly modeling return distributions can reduce performance. Instead, we argue that the use of integration for reading out values and dense velocity supervision at each step of this integration process for training improves TD learning via two mechanisms. First, it enables robust value prediction through \emph{test-time recovery}, whereby iterative computation through integration damp...
| Authors | Bhavya Agrawalla, Michal Nauman, Aviral Kumar |
|---|---|
| Primary category | cs.LG |
| Published | 4 March 2026 |
| Last updated at source | 8 May 2026 |
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