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fix typos: separate/reproduced/auditable, drop stray article

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Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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2026-06-02 12:07:58 +08:00
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@@ -30,7 +30,7 @@ Give each a one-line prior (rough credence) and its cheapest falsifier -- a `Che
Anchor priors on what's usually wrong (Part 7.2: data ~40%, loss ~20%, training ~15%, architecture ~10%, hyperparameters ~5%) -- but priors are a starting weight, not a verdict. A clue that points elsewhere overrides them outright: a traceback naming a line, a metric stuck while the loss is healthy (loss-metric misalignment, not data), or an init-loss that's exactly right all redirect you regardless of the ~40% data prior. Anchor priors on what's usually wrong (Part 7.2: data ~40%, loss ~20%, training ~15%, architecture ~10%, hyperparameters ~5%) -- but priors are a starting weight, not a verdict. A clue that points elsewhere overrides them outright: a traceback naming a line, a metric stuck while the loss is healthy (loss-metric misalignment, not data), or an init-loss that's exactly right all redirect you regardless of the ~40% data prior.
Make sure to seperate observations (to be faithfully reprocuded in an audtiable manner) and inferences. That way you can go back and rethink things without degrading the evidence. Make sure to separate observations (to be faithfully reproduced in an auditable manner) and inferences. That way you can go back and rethink things without degrading the evidence.
**Run the cheapest observation that splits your top hypotheses.** Not the most thorough experiment -- the most *discriminating* one (Rahtz: think more, experiment less, Part 1). To find it, forward-predict each hypothesis ("what would I see if this were the cause?"): a test is strong evidence only where the predictions diverge, and worthless where every hypothesis predicts the same outcome. Prefer the check whose result you'd bet on differently under each explanation -- a grad-norm line reading ~0 under "dead layer" but healthy under "LR too low" beats a 4-hour sweep that only confirms what you already believed. **Run the cheapest observation that splits your top hypotheses.** Not the most thorough experiment -- the most *discriminating* one (Rahtz: think more, experiment less, Part 1). To find it, forward-predict each hypothesis ("what would I see if this were the cause?"): a test is strong evidence only where the predictions diverge, and worthless where every hypothesis predicts the same outcome. Prefer the check whose result you'd bet on differently under each explanation -- a grad-norm line reading ~0 under "dead layer" but healthy under "LR too low" beats a 4-hour sweep that only confirms what you already believed.
@@ -39,7 +39,7 @@ But before you run a 10 minute test, remember it's much faster to step back, and
**Bisect the path to localize where it breaks.** Splitting hypotheses tells you which cause; bisecting tells you where. Data flows forward and gradients flow backward in a chain (input -> preprocess -> layers -> loss -> grads), so probe the midpoint instead of reading every step: is the value or gradient already wrong halfway through? Each probe halves the search space. The NaN-hunt (find the first module to produce a non-finite value, Part 6.2) is this move applied to NaNs; the same bisection localizes finite-but-wrong values, exploded grad norms, and dead activations. **Bisect the path to localize where it breaks.** Splitting hypotheses tells you which cause; bisecting tells you where. Data flows forward and gradients flow backward in a chain (input -> preprocess -> layers -> loss -> grads), so probe the midpoint instead of reading every step: is the value or gradient already wrong halfway through? Each probe halves the search space. The NaN-hunt (find the first module to produce a non-finite value, Part 6.2) is this move applied to NaNs; the same bisection localizes finite-but-wrong values, exploded grad norms, and dead activations.
**Then act, and only on what the observation pointed to.** If a cycle or two hasn't localized it, stop tuning and go read working code (next section) -- that's a better than another guess. **Then act, and only on what the observation pointed to.** If a cycle or two hasn't localized it, stop tuning and go read working code (next section) -- that's better than another guess.
Consult as reference, from inside this loop, never as a first move: triage tree (Part 6.3), hypothesis-generating lenses (Part 7.1), the metric-stuck decision tree (Part 5), RL specifics (`rl/SKILL.md`). Consult as reference, from inside this loop, never as a first move: triage tree (Part 6.3), hypothesis-generating lenses (Part 7.1), the metric-stuck decision tree (Part 5), RL specifics (`rl/SKILL.md`).