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Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs
Takeaway
Curate post-training data and environments against measured learning outcomes, using ablations to identify which choices improve reliability.
Summary
- Bespoke Labs treats reasoning examples, agent trajectories, and reinforcement-learning environments as forms of training data needed to improve agent reliability and autonomy.
- Its open work includes Curator, Bespoke Stratos, Open Thoughts, and contributions to Terminal-Bench, connecting data curation with actual post-training outcomes.
- Open Thoughts selects and mixes source questions, filters for quality or difficulty, generates teacher-model reasoning answers, and optionally filters or resamples those answers.
- Systematic ablations determine the curation recipe, with reported benchmark improvements as dataset size scales under that recipe.
post-trainingopen-thoughtsdata-curation
Original description
Mahesh Sathiamoorthy's pitch is to stand in the researcher's shoes: the hard part of post-training is not the algorithm but the data and the environments that feed it. As agents get pushed to run autonomously for hours, something eventually falls over, and reinforcement learning is the tool for stretching that reliability, but RL environments are really just data in a different shape. Bespoke Labs works on curating both, from supervised fine-tuning sets to the environments models learn in. He grounds it in OpenThoughts, the widely used reasoning dataset his team built, and the counterintuitive lessons that came out of curating it: diversity of reasoning traces matters, keeping multiple answers per question helps, and the obvious recipe often is not the best one. A favorite example is teaching a model to reason about credit card compliance, where fine-tuning on the right tagged data lifted the compliance metrics that a raw model kept getting wrong. The through line, supported by their Curator tooling, is that a disciplined curation stack, not just more compute, is what turns a base model into a capable post-trained one. Speaker info: https://x.com/madiator / smaheswaran https://smahesh.com Timestamps: 0:00 - Standing in the researcher's shoes 1:30 - Post-training at Bespoke Labs 3:13 - When agents fall over on long tasks 4:44 - RL environments as data 6:29 - Building OpenThoughts 7:36 - Finding a curation recipe 10:27 - Counterintuitive lessons 13:49 - A credit card compliance example 16:13 - Curating reasoning data with Curator 17:16 - The full curation stack