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Lean4AgenticRL: Advancing Agentic RL with Verifiable Multi-level Process Rewards

Enhancing the agentic capabilities of Large Language Models (LLMs) to tackle complex software engineering (SWE) and general terminal-usage problems is a pressing challenge. Current state-of-the-art LLMs often go through large-scale agentic Reinforcement Learning (RL).

By Ruida Wang, Gerald Friedland, Tong Zhang, Chris KongarXiv cs.LG

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Originally published by arXiv cs.LG on Oct 19, 2026.

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