Ablation of pipeline components to quantify their relative contributions

Determine the relative importance of the following components within the synthetic environment generation and training pipeline—dataset grounding via HuggingFace validation, the self-debug loop, success-only trajectory filtering, trajectory length truncation, and teacher model quality—in contributing to the observed performance gains.

Background

The method aggregates several design choices into a single training pipeline, including dataset validation against HuggingFace, an automated self-debug loop for task verification, filtering trajectories to those with at least one successful submission, truncating long trajectories, and using a high-capability teacher model (GPT-5) to generate trajectories.

While the pipeline yields performance improvements on MLGym after supervised fine-tuning, the authors did not perform ablations to isolate the effect of each component. Consequently, the relative contribution of each component to the overall gains is unknown.