Policy-Adaptive Credit Assignment for Spatial Reasoning with Verifiable Rewards
Image-based spatial reasoning requires perception, decomposition, and inference, but the stage that limits accuracy can change during training. Answer-level rewards neither localize failures to individual stages nor provide discriminative group-relative feedback when all rollouts receive the same reward. Process supervision provides intermediate feedback, but fixed weights cannot adapt reward allocation to these changing bottlenecks. To address this gap, we propose CALIPER, an adaptive process-reward framework supported by SpatialTrace, a corpus of stage-decomposed spatial reasoning traces. CALIPER jointly estimates the associations between stage-wise reference coverage and answer correct...
| Authors | Hailiang Yang, Shengchao Chen |
|---|---|
| Primary category | cs.CV |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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