--- schema: 1 type: video title: >- Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs 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 - https://linkedin.com/in/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 canonical: >- https://liquidgold.cc/channel/aidotengineer/explore/video/data-and-environment-curation-for-post-training-llm-94d5cbf84316 markdown: >- https://liquidgold.cc/channel/aidotengineer/video/data-and-environment-curation-for-post-training-llm-94d5cbf84316.md llms: null parent: 'https://liquidgold.cc/channel/aidotengineer.md' channel: aidotengineer updated: '2026-10-24T06:29:17.931Z' language: null children: [] videoId: ewtOo0scUh0 durationSeconds: 1152 publishedAt: '2026-07-31T22:00:06.000Z' searchChunks: - ordinal: 0 text: null startSeconds: 24 endSeconds: 90 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=24' heldReason: rights - ordinal: 1 text: null startSeconds: 103 endSeconds: 193 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=103' heldReason: rights - ordinal: 2 text: null startSeconds: 103 endSeconds: 193 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=103' heldReason: rights - ordinal: 3 text: null startSeconds: 203 endSeconds: 295 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=203' heldReason: rights - ordinal: 4 text: null startSeconds: 295 endSeconds: 307 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=295' heldReason: rights - ordinal: 5 text: null startSeconds: 307 endSeconds: 396 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=307' heldReason: rights - ordinal: 6 text: null startSeconds: 407 endSeconds: 464 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=407' heldReason: rights - ordinal: 7 text: null startSeconds: 475 endSeconds: 636 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=475' heldReason: rights - ordinal: 8 text: null startSeconds: 475 endSeconds: 636 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=475' heldReason: rights - ordinal: 9 text: null startSeconds: 648 endSeconds: 835 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=648' heldReason: rights - ordinal: 10 text: null startSeconds: 648 endSeconds: 835 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=648' heldReason: rights - ordinal: 11 text: null startSeconds: 648 endSeconds: 835 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=648' heldReason: rights - ordinal: 12 text: null startSeconds: 846 endSeconds: 981 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=846' heldReason: rights - ordinal: 13 text: null startSeconds: 846 endSeconds: 981 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=846' heldReason: rights - ordinal: 14 text: null startSeconds: 846 endSeconds: 981 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=846' heldReason: rights - ordinal: 15 text: null startSeconds: 981 endSeconds: 992 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=981' heldReason: rights - ordinal: 16 text: null startSeconds: 1050 endSeconds: 1152 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=1050' heldReason: rights - ordinal: 17 text: null startSeconds: 1050 endSeconds: 1152 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=1050' heldReason: rights - ordinal: 18 text: null startSeconds: 1050 endSeconds: 1152 watch: 'https://liquidgold.cc/watch/ewtOo0scUh0?t=1050' heldReason: rights --- # Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs 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 - https://linkedin.com/in/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 Canonical: https://liquidgold.cc/channel/aidotengineer/explore/video/data-and-environment-curation-for-post-training-llm-94d5cbf84316 · Channel: [AI Engineer](https://liquidgold.cc/channel/aidotengineer.md) > Search chunk text is held until public-source provenance is verified. ## Document chunks > Missing: no stored document chunks are available.