Verl

Large language models are finally learning to work the tools instead of merely talking about them. RLFactory proposes a clean way to post‑train LLMs for multi‑turn tool use by rebuilding the reinforcement learning loop around tool feedback, not just text. The result: quicker training, higher stability, and a framework teams can actually adopt. Why this matters (and where prior setups struggle) Most RL-for-LLMs treat the environment as pure text: the model thinks, emits tokens, gets a scalar reward. But real tasks—searching, querying databases, compiling code, booking travel—depend on external tools that return structured results, fail intermittently, and vary in latency and format. Hard problems emerge: ...