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  "text": "Earlier this month, Apple introduced Simple Self-Distillation: a fine-tuning method that improves models on coding tasks just by sampling from the model and training on its own outputs with plain cross-entropy\n\nand… it's already supported in TRL, built by @krasul. you can really feel the pace of development in the team 🐎\n\npaper by @onloglogn, @richard_baihe,\n@UnderGroundJeg, Navdeep Jaitly, @trebolloc, @YizheZhangNLP\nat Apple 🍎\n\nhow it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed\n\nyou can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder):\nhttps://t.co/zizfISD6bq\nor benchmark a checkpoint with the eval script:\nhttps://t.co/mKlafTyKSe\n\none neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help\n\nwant to dig deeper?\n\npaper: https://t.co/aj1ZAcr8Mw\ntrainer docs: https://t.co/TNVz93kZi9",
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