Researchers have discovered that directly fine-tuning large language models on trajectories generated by heavily scaffolded autonomous agents can inadvertently bake in those scaffolding dependencies, leading to poor performance when deployed in real-world environments. A new technique, Recursive Self-Rewrite (RSR), addresses this by separating the exploration phase (using various harnesses) from the execution phase, creating a pipeline that generates clean training data free of harness-specific artifacts. This approach, demonstrated with Qwen-3.8-27B, significantly improved performance on several terminal task benchmarks, and the resulting model weights are now publicly available.
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