Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
A new method called Self-Verified Distillation (SVD) enables LLMs to self-improve using only unlabeled prompts, without external feedback. The model generates candidate solutions, filters them through a three-stage verification cascade, and trains on the curated data. Experiments on Qwen3 models show significant gains across math, science, and coding benchmarks.
Article intelligence
Key points
- SVD uses cycle-consistency, factuality, and correctness checks to filter self-generated solutions.
- More candidate samples and larger verification budgets yield higher-quality training data.
- Qwen3-4B improved by +16.7 in math, +11.1 in science, and +8.3 in coding (pass@1).
- SVD outperforms test-time compute baselines while requiring only a single inference at test time.
Why it matters
This matters because SVD uses cycle-consistency, factuality, and correctness checks to filter self-generated solutions.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26132] Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
[Submitted on 20 May 2026]
Title:Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
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Abstract:Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools? We study this setting starting only from unlabeled seed questions with no ground-truth solutions, across three reasoning domains: math, science, and coding. We propose Self-Verified Distillation, a simple post-training refinement algorithm in which the model generates candidate solutions to these seed questions, filters them using prompt-based self-verification, and trains on the resulting self-curated dataset. Inspired by the UQ benchmark's use of multiple validators to screen candidate answers to hard unsolved questions, we adapt this validation-based filtering idea to self-training: the model filters its own generated solutions through a three-stage cascade of cycle-consistency, factuality, and correctness checks, accepting a solution only if it passes all stages with unanimous judge votes. We find that sampling more candidate generations and using a larger verification budget during training data construction produces higher-quality self-curated data and, in turn, better reasoning models. We then train Qwen3 models at multiple scales with Self-Verified Distillation and obtain gains across all three domains. For Qwen3-4B, our method improves aggregate held-out pass@1 by +16.7 points in math (AIME26 and HMMT), +11.1 points in science (GPQA Diamond and HLE), and +8.3 points in coding (LCBv5 and LCBv6), with gains also extending to 0.6B and 8B models. Compared to our test-time-only baseline (UQ-TTC), which improves performance by spending extra compute at inference time, Self-Verified Distillation achieves better performance in most settings while requiring only a single inference call at test time.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.26132 [cs.CL]
(or arXiv:2605.26132v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.26132
arXiv-issued DOI via DataCite
Submission history
From: Tony Lee [view email] [v1] Wed, 20 May 2026 17:26:10 UTC (2,264 KB)
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