SPOT: Sparse Probing and Outcome Calibration for On-Policy Distillation
Quick summary
arXiv:2608.04419v1 Announce Type: cross Abstract: On-policy distillation (OPD) provides dense teacher supervision on student-generated trajectories, but standard reverse-KL training can assign insufficient probability to other plausible continuations. Teacher entropy alone does not reveal whether uncertainty is concentrated among a few plausible next tokens or dispersed over a long probability tail, nor whether the student already represents those candidates well. Moreover, local teacher probabilities may not predict downstream success. We introduce Sparse Probing and Outcome-calibrated Target
Key takeaways
- arXiv:2608.04419v1 Announce Type: cross Abstract: On-policy distillation (OPD) provides dense teacher supervision on student-generated trajectories, but standard reverse-KL training can assign insufficient probability to other plausible continuations.
- Teacher entropy alone does not reveal whether uncertainty is concentrated among a few plausible next tokens or dispersed over a long probability tail, nor whether the student already represents those candidates well.
- Moreover, local teacher probabilities may not predict downstream success.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “SPOT: Sparse Probing and Outcome Calibration for On-Policy Distillation” may reshape data collection, model training, output accountability and market access.
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