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title: Data and Environment Curation for Post-Training LLMs —...
description: Mahesh Sathiamoorthy, co-founder of Bespoke Labs, presents research on data and environment curation for post-training LLMs. The talk covers the Open Thoughts...
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# Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 19 min read · 0 upvotes · 0 comments

## Summary

Mahesh Sathiamoorthy, co-founder of Bespoke Labs, presents research on data and environment curation for post-training LLMs. The talk covers the Open Thoughts project — a reasoning dataset created in collaboration with Stanford, UC Berkeley, and others — detailing the curation pipeline: sourcing prompts, mixing datasets, filtering questions, generating answers with teacher models, and running ablations. Key counterintuitive findings include: sampling multiple answers per question outperforms more questions answered once, and stronger models are not always better teachers. The talk extends to Open Thoughts Agents, applying similar curation principles to RL environments for agent training, where SFT still contributes significantly before RL adds the final gains. A concrete enterprise case study with Credit Karma shows how post-training improved compliance, reduced latency, and lowered costs. The Curator tooling and a broader reference stack for building RL environments and post-training agents are also introduced.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=ewtOo0scUh0>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning)

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