Deep Dive: How Agent Harnesses Improve LLM Generalization via Trajectory Shaping
a1zhang · x · 2026-07-23
Further clarifying the experimental design regarding model generalization, the author explained that evaluations deliberately introduced variations in task length or topic. The experiments demonstrate that RL training on a model with a harness generalizes better out-of-distribution than directly training the base LLM. While arbitrarily using standard harnesses might not yield the same lift, the core value lies in the harness's ability to shape token-by-token input trajectories for the underlying LM's individual calls. Experimental observations confirm that these shaped trajectories are crucial for the model's performance on evaluation tasks.
Related event: LLM Generalization Debate: Intrinsic Model or Harness Contribution?(11 posts)→
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