ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context
Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the way matter. Progress Reward Models (PRMs) score how far a task has come at every step, and serve as dense rewards, verifiers and monitors. Yet in long tasks the current frame alone often cannot tell how far the task has come, because progress depends on what happened before. We call this problem context-dependent progress estimation. Existing benchmarks on progress estimation mostly focus on short tasks whose progress can be read from the current observation, and whether PRMs can estimate progress when context is needed remains underexplored...
| Authors | Jianshu Zhang, Keliang Wu, Chengxuan Qian et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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