Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning
arXiv:2605.26684v1 Announce Type: new Abstract: Group-based reinforcement learning (RL) methods have achieved remarkable success in improving the performance of large language models (LLMs) and have been rapidly extended to agentic tasks. However, their credit assignment relies heavily on coarse-grained trajectory-level attribution according to final outcomes, making it difficult to capture the contribution of individual steps, such as valuable steps obscured within failed trajectories. To uncover latent information and enable more faithful step-level credit assignment, we propose Graph-based
This development arises from the rapid extension of group-based reinforcement learning to agentic tasks, revealing limitations in current credit assignment methodologies for complex AI systems.
Improving credit assignment in agentic reinforcement learning could dramatically accelerate AI capabilities, leading to more robust and autonomous agents capable of nuanced task execution.
The ability to attribute value to individual steps within complex AI agent trajectories, rather than just final outcomes, fundamentally changes how these systems can be trained and optimized.
- · AI platform developers
- · Robotics companies
- · Enterprise automation solution providers
- · Researchers in AI/ML
- · Companies reliant on simple, rules-based automation
- · Legacy software integrators
More efficient and capable AI agents will emerge, able to perform multi-step, complex operations with greater reliability.
This improved agent capability will accelerate the automation of white-collar tasks and complex decision processes within various industries.
The enhanced autonomy and reliability of AI agents could reshape labor markets and drive demand for entirely new categories of AI-enabled services and products.
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Read at arXiv cs.LG