Summary
Fine-grained credit assignment in reinforcement learning means giving detailed feedback during learning, instead of only judging success at the end. The authors explored when this detailed feedback improves training for AI systems that edit videos, a task involving many steps and different kinds of reasoning. They found it helps a lot on very complex editing tasks with long sequences of actions but less so on simpler tasks. However, if the detailed feedback is not gradually reduced, it can lead the AI to overthink or repeat unnecessary steps. Another technique meant to improve learning, on-policy self-distillation, showed little benefit and was sensitive to extra information used.
What this means in practice
- •For video editing software developers: Improve AI-assisted video editing tools by applying fine-grained credit assignments in complex, long-sequence editing tasks like fine cuts.$Commercial implications: Enables advanced AI video editing features that better manage complex sequences, improving automation quality for professional editing software.
- •For multimodal ai developers: Design reinforcement learning systems that adjust process reward weights over time to avoid reward hacking in tasks requiring extended reasoning and tool use.
Abstract
Reinforcement learning (RL) has shown promise for training multimodal agents to reason and use tools. However, it remains unclear when fine-grained credit assignment improves learning and when it fails to improve task performance. Video editing provides a useful testbed for this question, combining multimodal perception, long-horizon planning, and tool use with delayed outcome feedback. We construct four movie-trailer editing task types---Content Selection, Narrative Sequencing, Rough Cut, and Fine Cut---that reflect real-world editing workflows and span different levels of task complexity and interaction horizon. Decaying process rewards provides the largest benefit on Fine Cut, with a 45.4% relative gain over outcome-only GRPO on tasks averaging more than 60 turns under outcome-only GRPO. On the three shorter-horizon task types averaging 30--40 turns under the same baseline, outcome-only GRPO remains competitive, with decaying process rewards yielding at most a 10.4% relative gain. Process rewards from an LLM judge initially improve tool use and encourage visual inspection. As training progresses, however, GRPO+PRM with a fixed process-reward weight increasingly exhibits excessive reasoning and redundant tool calls: process rewards continue to rise while task completion rates and outcome scores decline. Decaying the process-reward weight to zero mitigates this reward-hacking behavior while preserving the early gains. Despite dense token-level supervision, on-policy self-distillation (OPSD) yields little improvement or substantial degradation and is highly sensitive to the choice of privileged information. Our analysis reveals differences in teacher--student disagreement and gradient magnitudes, and suggests possible dilution of informative signals over long trajectories.