PROOF-Gen: From Optimized Data to Better Distillation
Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher’s passing trajectories, discard the rest) and each...
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Qué ocurrió
Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher’s passing trajectories, discard the rest) and each...
Por que importa
The development may change operating conditions or market expectations around AI. Further confirmation and measurable outcomes matter.
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1 articulos · 1 informe original · 1 independientes
- Apple Machine Learning ResearchFuente primaria · Respalda · EN · 100%PROOF-Gen: From Optimized Data to Better Distillation ↗
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- PROOF-Gen: From Optimized Data to Better Distillation Observado
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