MPCoT: Reward-Guided Multi-Path Latent Reasoning for Test-Time Scalable Vision-Language-Action
Vision-Language-Action (VLA) policies remain brittle in long-horizon control, where one-pass action decoding offers limited inference-time deliberation. Explicit chain-of-thought adds reasoning depth but incurs token-generation latency and an indirect text-to-action interface. We propose MPCoT, a reward-guided multi-path latent reasoning framework with configurable depth K and width M. MPCoT initializes M latent hypotheses, refines them for K weight-tied steps, and softly aggregates them before action decoding. A training-only path-preference objective combines expert-trajectory consistency, frozen Qwen3-VL progress scores, and endpoint-consistency feedback to supervise the path scorer. U...
| Authors | Boyang Zhang, Lianlei Shan |
|---|---|
| Primary category | cs.RO |
| Published | 4 June 2026 |
| Last updated at source | 15 September 2026 |
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