ClawGym II investigates black‑box RL in agent harness
agents reinforcement-learning training
| Source: HF Papers | Original article
Researchers investigate black‑box reinforcement learning on agent harnesses, a technique that has boosted long‑horizon task performance but remains largely unexplored due to scaling challenges.
ClawGym II unveils a unified black‑box reinforcement‑learning (RL) framework that tackles the long‑standing challenge of training agents through complex harnesses. Building on the earlier ClawGym system, the new work demonstrates that sandbox execution, trajectory reconstruction and mix‑harness training can together deliver stable, scalable optimisation of general agents on long‑horizon tasks.
The significance lies in moving beyond the supervised fine‑tuning that has powered ClawGym‑Agents so far. Earlier versions relied on 24.5 K high‑fidelity black‑box rollout trajectories collected from teacher models such as MiniMax‑M2.5 and GLM‑5.1, then filtered by verifier scores. While those trajectories proved effective for supervised learning, reinforcement learning through intricate harnesses remained largely unexplored because scaling to long‑horizon interactions introduces fundamental challenges. By treating the harness as a black box and employing a lightweight sandbox‑parallel pipeline, ClawGym II shows that agents can learn to coordinate their actions with the environment without direct access to internal dynamics, opening the door to more robust, tool‑intensive behaviours.
What to watch next includes the community’s response to the open‑source repository, where researchers can experiment with the mix‑harness approach on their own tasks. Follow‑up studies are likely to probe how the framework scales to even longer horizons, integrates with task synthesis pipelines, and supports diagnostic evaluation across diverse domains. Success could accelerate the deployment of autonomous systems that require sophisticated, multi‑step planning—ranging from robotic manipulation to simulated environments—by leveraging black‑box RL without sacrificing stability or performance.
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