When to Re-Commit: Temporal Abstraction Discovery for Long-Horizon Vision-Language Reasoning
For long-horizon reasoning in vision-language models, this work addresses the fixed-commitment bottleneck by enabling adaptive replanning, yielding substantial gains over fixed baselines.
The paper introduces a method for learning state-conditioned commitment depth in vision-language policies, achieving up to 12.5% higher solve rates and 25% fewer actions on long-horizon tasks, outperforming GPT-5.5 and Claude Sonnet.
Long-horizon reasoning requires deciding not only what actions to take, but how deeply to commit before the next observation. We formalize this as \emph{commitment depth}: the number of primitive actions executed open-loop between replans. Commitment depth induces a trade-off between replanning cost and compounding execution error, yet most existing long-horizon systems fix it as a hand-designed scalar. In this work, we instead treat commitment depth as a learnable, state-conditioned variable of the policy itself. We instantiate this within a model-native vision--language policy that jointly predicts both what to execute and for how long. Across Sliding Puzzle and Sokoban, the resulting adaptive policy Pareto-dominates every non-degenerate fixed-depth baseline, achieving up to 12.5 percentage points higher solve rate while using approximately 25\% fewer primitive actions per episode. Despite using a 7B backbone, our method outperforms GPT-5.5 and Claude Sonnet on both tasks, while every tested open-weight vision--language model achieves 0\% zero-shot success. We further present a theoretical analysis showing that, under the standard commitment-depth surrogate, state-conditioned commitment strictly dominates any fixed depth whenever the locally optimal depth varies across states.