Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning
📰 ArXiv cs.AI
Learn to improve credit assignment in agentic reinforcement learning using graph-based methods beyond trajectory-level attribution
Action Steps
- Apply graph-based credit assignment to agentic reinforcement learning tasks
- Use node-level and edge-level attribution to capture individual step contributions
- Implement a graph neural network to learn credit assignment
- Evaluate the performance of graph-based credit assignment compared to trajectory-level attribution
- Refine the graph structure to improve credit assignment accuracy
Who Needs to Know This
Researchers and engineers working on reinforcement learning and large language models can benefit from this approach to improve credit assignment and overall performance
Key Insight
💡 Graph-based credit assignment can capture individual step contributions beyond trajectory-level attribution
Share This
🤖 Improve credit assignment in agentic RL with graph-based methods! 📈
Key Takeaways
Learn to improve credit assignment in agentic reinforcement learning using graph-based methods beyond trajectory-level attribution
Full Article
Title: Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning
Abstract:
arXiv:2605.26684v1 Announce Type: cross 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 unc
Abstract:
arXiv:2605.26684v1 Announce Type: cross 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 unc
DeepCamp AI