RAFT: Reasoning-Aligned Fine-Tuning Improves Robustness of VLA Post-Training
Adapting a generalist vision-language-action (VLA) policy to a target task with limited demonstrations can improve nominal task success while reducing robustness to variations in appearance, objects, and instructions. We introduce reasoning-aligned fine-tuning (RAFT), a two-stage post-training procedure: RAFT first trains low-rank adapters on task-relevant reasoning annotations, then freezes them while fine-tuning the policy on action demonstrations. On a physical DROID robot, RAFT achieves $75.0%$ out-of-distribution success, compared with $27.9%$ for plain fine-tuning; on a multi-stage bimanual YAM task, it reaches $70.0%$ mean success, compared with $36.0%$ for the strongest baseline. RAFT also improves success under visual, object, and instruction shifts in simulated pick-and-place and open-drawer tasks. Compared with plain fine-tuning, RAFT policies place more attention on annotated task regions and yield higher linear-probe accuracy for object identity in later layers. A broader reasoning-data mixture further improves downstream success across demonstration budgets.
追踪学者：Dhruv Shah；兴趣：vision-language-action (VLA) models and foundation models for robots