LLM Agents on SoloSoft

OpenManus-RL: Reinforcement Learning Tuning for LLM Agents

Fri, 01 May 2026 00:00:00 +0000

OpenManus-RL is an open-source research project at the intersection of reinforcement learning and LLM agent systems, developed collaboratively by Ulab-UIUC (University of Illinois Urbana-Champaign) and MetaGPT. The project provides a comprehensive framework for reinforcement learning tuning of LLM-based agents, with implementations of GRPO (Group Relative Policy Optimization), supervised fine-tuning (SFT), and advanced rollout strategies designed specifically for agentic tasks.

As LLM agents become increasingly capable of complex multi-step reasoning and tool use, the need for targeted reinforcement learning optimization has grown dramatically. OpenManus-RL addresses this by providing a modular, reproducible pipeline for training agents on agent-specific tasks, with built-in support for diverse environments including software engineering (SWE-Bench), web navigation (WebArena), and general tool use.

Verifiers: Modular RL Environment Library for Training LLM Agents

Fri, 01 May 2026 00:00:00 +0000

Verifiers is a modular Python library developed by PrimeIntellect-ai that provides a comprehensive framework for creating reinforcement learning environments tailored to training LLM agents. Designed for researchers and practitioners working on RL-based LLM alignment and agent optimization, Verifiers offers a clean, composable API with components for parsing model outputs, evaluating responses against rubrics, computing rewards, and running GRPO-based training loops.

The library addresses a growing need in the AI research community: as RL-based methods like GRPO, PPO, and rejection sampling become standard for LLM fine-tuning, researchers need standardized, reusable environment components rather than building training infrastructure from scratch for each experiment. Verifiers provides exactly this – a modular toolkit where environments are assembled from interchangeable building blocks.