ClawLoop: Self-Improving Agents via Harness-Native Training Loop
The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents operate in sandbox workspaces and interact directly with file systems and shells. While numerous post-training pipelines exist for tool-use agents, their counterparts for harness-based agents remain scarce and are predominantly on
Reading in Plain English mode — toggle below to switch
The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents operate in sandbox workspaces and interact directly with file systems and shells. While numerous post-training pipelines exist for tool-use agents, their counterparts for harness-based agents remain scarce and are predominantly on
This research introduces a self-improving training loop for harness-based LLM agents, moving beyond one-shot, teacher-dependent methods. For ML engineers, this means developing more robust and autonomous agents that can continuously learn and adapt in complex environments, reducing reliance on constant human intervention or external models.