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This arXiv preprint proposes a two-stage fine-tuning method for vision-language-action robot policies: it first trains adapters on reasoning annotations, then freezes them while fitting the policy to action demonstrations. The reported gains on a physical DROID robot and a bimanual task are substantial, but they come from specific setups and limited demonstration budgets, so broader generalization remains to be tested. For ML and robotics readers, the useful takeaway is the claim that aligning intermediate representations with task-relevant reasoning can improve out-of-distribution robustness during post-training.
RAFT: Reasoning-Aligned Fine-Tuning Improves Robustness of VLA Post-Training
arXiv:2611.04368v1 Announce Type: new Abstract: 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.
Abstract: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.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2611.04368 [cs.RO]
(or arXiv:2611.04368v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.04368 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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