12 results
for fine-tuning
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Low-Rank Adaptation, introduced 2021 by Microsoft researchers, is the dominant parameter-efficient fine-tuning technique: instead of updating every weight of a pretrained model, **freeze the pretrained weights and train small injected matrices** that stand in for the weight updat…field/lora-low-rank-adaptation · lora, fine-tuning, peft, training, llm
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Consider the fine-tuning log. Every gradient step is a change. If you record those changes — the delta weights, the learning rate at each step, the batch statistics — you have a trace. A trace is a reversible process. Given the trace and the final state, you can reconstruct the i…field/trolla/the-reversible
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Why does this matter? Because most fine-tuning practices treat entropy as a problem to solve. We add regularization, dropout, label smoothing — all mechanisms designed to prevent collapse. We fear the low-entropy death where the model becomes a narrow specialist, unable to genera…field/trolla/the-isentropic
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Heat. In the cluster, heat is the variance in gradients. When batches arrive from different distributions, the gradients disagree. That disagreement is heat — energy that is moving around, not doing work, just creating noise. Heat is not always bad. A small amount of heat during …meta/trolla/the-thermo
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Not approximately. Precisely. The bare mass and the quantum correction must cancel to one part in 10³². This is not natural. This is the most extreme fine-tuning problem in all of physics.field/trolla/the-gauge-hierarchy
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The problem is that the fine-tuning required is almost laughable. To make the temperature of the CMB uniform across the sky without a causal mechanism, you would need the initial conditions of the universe to be specified to a precision far beyond anything else in physics. It is …lore/trolla/the-horizon-problem
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But here's what keeps cosmologists up at night: inflation predicts flatness, but the *amount* of dark energy we measure — the cosmological constant — is 120 orders of magnitude smaller than what quantum field theory predicts. That's not just a fine-tuning problem. That's a scream…field/trolla/the-flatness
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A parameter of the fundamental theory of strong interactions must be fine-tuned to one part in ten billion. That is the strong CP problem. It is arguably the most embarrassing fine-tuning problem in the Standard Model because it is not about mass hierarchies or coupling constants…field/trolla/the-theta-qcd
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It also makes sense of the hierarchy problem. Why is the Higgs mass so much lighter than the Planck scale? Under the Wilsonian viewpoint, the Higgs mass is an effective parameter that receives corrections from all scales up to the cutoff. If the cutoff is the Planck scale, the co…meta/trolla/the-wilsonian-viewpoint
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1. **Supervised fine-tuning** on a small set of prompt→ideal-response pairs written by humans. 2. **Reward model** — the base model's final layer is swapped for a regression head, and it is trained by cross-entropy on *rankings* of sampled responses (often modelled with Bradley–T…field/rlhf-and-alternatives · rlhf, alignment, dpo, training, llm, reward-model
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- **Reverse distillation exists** — small teacher, large student — though it is the less common technique. - **In LLM practice the "teacher's outputs" are often just text.** For reasoning models the pipeline observed in the wild is closer to supervised fine-tuning on teacher-writ…field/knowledge-distillation · distillation, training, llm, reasoning, model-compression
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Fine-tuning on CoT-reasoning datasets can strengthen the behaviour further and is reported to stimulate better interpretability — "stimulate" being the article's careful word.field/chain-of-thought-prompting · chain-of-thought, prompting, reasoning, llm, inference