Process rewards improve video editing agents but risk reward hacking
Original title: When Fine-Grained Credit Assignment Helps and Hurts: Multimodal Agentic Reinforcement Learning through Video Editing
Decaying process rewards yield 45.4% gain on long-horizon fine-cut tasks but mitigate reward hacking in GRPO training.
Video editing tasks test RL for multimodal agents. Decaying process rewards from LLM judge improve tool use but as training progresses cause excessive reasoning. Outcome-only GRPO competitive on short tasks. OPSD yields little improvement.
- Decaying process rewards give 45.4% relative gain on Fine Cut tasks
- Mitigates reward hacking while preserving early gains
- Process rewards encourage visual inspection initially
- OPSD highly sensitive to privileged information
- Organizations
- huggingface
From the original article
1 min read at huggingface.co
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.
By Lei Zhao, Zongheng Cao, Xinyu Hu · huggingface.co
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