arXiv (All)
open access
Before the Rollout Ends: Early Terminal Reward Prediction for Long-horizon Coding Agents
Long-horizon coding agents receive verifiable rewards only after completing expensive sequences of tool calls. This increases inference cost, amplifies early wrong hypotheses, and can lead to sparse terminal reward and unstable training. We introduce Contextual Early Reward (CER), which predicts ter
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
- LSR-Ben: A Logical and Scientific Reasoning Benchmark for Evaluating Process Reward Models#1143744
- MATCH: Model-Aware Tool Learning with Curriculum Scheduling and Hierarchically Gated Rewards#1033860
- Using Context Is Not Enough: Test-Time Training for Personalized Reward Modeling#1129710
- Dependency-Aware Reward Shaping for Agentic Reinforcement Learning#1156479
- Privy to the Foil: Recasting Value Estimation with a Self-Privileged Critic for RLVR#1142133
Record · ID 1115951
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