arXiv (All)
open access
Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning
Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment of trajectory-level advantages to all policy tokens fails to distinguish consequential decisions from less relevant ones, obscuring which intermediate d
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- EPIG-Tree: Compute-Optimal Branching for Gradient-Efficient Reinforcement Learning#1033786
- SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents#1132140
- Back to the Definition: Estimating Step-Level Advantages via Trajectory Graphs for Agentic Reinforcement Learning#1076619
- Trust the Critic More#1144214
- ThinkPrior: Zero-Rollout Difficulty Priors for Cold-Start Prompt Selection in RLVR#1070939
Record · ID 1131020
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive