Flux-OPD: On-Policy Distillation with Evolving Contexts
Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. Contexts can convey such preferences, yet provide little additional supervision once distilled into the student, motivating contexts that evolve with student performance. However, directly using evolving contexts as in-training supervision results in an unstable distillation target and conflicting distributions, requiring mechanisms to stabilize t
Record details
Published: 29 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Regulation · Children & education
Retrieved: 31 July 2026
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ethics.ai (29 July 2026), “Flux-OPD: On-Policy Distillation with Evolving Contexts,” evidence record 14889, https://ethics.ai/record/14889 (originally published by HuggingFace Daily Papers).
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