On-Policy Self-Training: Data-Free Self-Improvement in Large Language Models
The paper introduces On-Policy Self-Training (OPST), which reformulates on-policy self-distillation as a self-training framework. Guided only by coarse, high-level topic descriptions, a model generates its own question-answer pairs and privileged reasoning traces without human-curated prompts or answers. A pseudo-verification filter keeps a problem only if the model reproduces its answer in a fresh context and under a paraphrase. The authors report self-improvement on language reasoning, retention on out-of-domain benchmarks when mixing generation domains, and transfer to multimodal inputs via Visual OPST.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper introduces On-Policy Self-Training (OPST), which reformulates on-policy self-distillation as a self-training framework. Guided only by coarse, high-level topic descriptions, a model generates its own question-answer pairs and privileged reasoning traces without human-curated prompts or answers. A pseudo-verification filter keeps a problem only if the model reproduces its answer in a fresh context and under a paraphrase. The authors report self-improvement on language reasoning, retention on out-of-domain benchmarks when mixing generation domains, and transfer to multimodal inputs via Visual OPST.
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Key findings from the stored brief
- OPST builds its own curriculum of queries and reasoning traces from only coarse, high-level topic descriptions.
- A pseudo-verification filter keeps a problem only if the model reproduces its answer in a fresh context and under a paraphrase.
- Self-distillation on fully synthetic privileged information improves across multiple domains while retaining capabilities on untrained domains.
- The framework transfers to multimodal inputs via Visual OPST, improving visual question answering.
Source claims not independently checked; the pseudo-verification filter only checks self-consistency, not correctness against ground truth.
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Evidence & sources
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Why this may matter
If models can improve without human-written questions or answers, post-training could rely less on curated datasets. The approach suggests foundation models may hold latent capabilities that can be amplified through self-generated supervision.
Limits and open questions
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