קידום LLM אחרי אימון - עיצוב נתונים וסביבה

תקציר מקורי באנגליתMahesh 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 teach