arXiv (All)
open access
Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents
Rubric-based tasks are increasingly addressed through reinforcement learning (RL), with rubric scores used as training rewards. However, these rewards typically supervise final answers without distinguishing the contributions of intermediate decisions. Many existing credit assignment methods rely on
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
- TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents#620568
- TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents#618889
- Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts#1129470
- SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents#1132140
- Cliff: Learning Process Rewards from the First Mistake#924942
Record · ID 1119369
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