arXiv (All)
open access
RewardWeaver: Long-Horizon Interactive Learning for Language Agents via Self-Evolving Reward Adaptation
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in domains where task outcomes can be reliably evaluated, but long-horizon interaction remains challenging due to sparse terminal feedback and difficult credit assignment. Process rewards provide denser supervision
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
- Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts#1129470
- My FAULT: Self-Diagnosis as Credit Assignment in Self-Evolving Agentic Reinforcement Learning#1156096
- Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning#1297856
- Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning#1291860
- SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents#1132140
Record · ID 1234487
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