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- refs/ -> references/, the folder name the Agent Skills spec uses and the one Hermes skips when it walks for nested skills. - rl and pinn declared name: rl and name: pinn, which are global names in a flat skill namespace. Now ml-debug-rl and ml-debug-pinn. They also called themselves sub-skills of 'ml-debugging', which is not this skill's name. - Drop the dead link to SKILL_old.md. It moved into gitignored slop/, so the link was broken for anyone who cloned. - Route references/llm_judge_litreview.md, the one reference SKILL.md never named. - Description leads with the trigger situations. Hermes truncates it to 57 chars. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
58 lines
3.0 KiB
Markdown
58 lines
3.0 KiB
Markdown
# Why won't this metric move?
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Appendix to the [ML Debugging skill](../SKILL.md). When a quantity you're optimizing plateaus, these are ideas for telling *why*, not a flowchart to obey. They apply to most training setups, but they're suggestions; your project may not fit them.
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The useful split is three questions, cheapest first.
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## 1. Is the gradient nonzero at the metric level?
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```py
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metric_val = torch.tensor(current_value, requires_grad=True)
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loss = loss_fn(metric_val)
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loss.backward()
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print(f"d(loss)/d(metric) = {metric_val.grad}")
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```
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- ~0: the loss doesn't care about this metric at the current operating point. Maybe saturated (log1p of a huge value), in a dead zone, or the metric is disconnected from the loss.
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- large: the loss is trying to move it. The problem is downstream.
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## 2. Can the parameter even change the metric?
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Trace the chain `loss -> metric -> ... -> parameter`. The metric is a function of intermediate quantities, which are functions of learned parameters. Look at `d(metric)/d(parameter)`:
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- Analytically: is there a structural reason this derivative is ~0? (e.g. a rotation of V can't change span(U).)
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- Empirically: disable the loss term (set its coefficient to 0). Does the metric reach the same value anyway? If yes, the optimization never moved it; it's a structural ceiling, and you need a different parameterization, not a different loss weight.
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## 3. Is something else fighting it?
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If the gradient is nonzero and the parameter *can* change the metric:
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- Competing loss terms: compute each component's gradient on the shared parameter separately. Opposite-sign gradients cancel.
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- Optimizer state: AdamW momentum from earlier training can resist a direction change. Try resetting optimizer state or a warmup.
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- Conditioning: if the metric needs coordinated changes across many parameters (rotating several layers at once), the per-parameter gradient may be too small even when the aggregate signal is large.
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## A rough map (a guide, not a verdict)
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| d(loss)/d(metric) | d(metric)/d(param) | Same value with the term off? | Reading |
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|---|---|---|---|
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| ~0 | any | any | Loss saturated or disconnected; reconsider the loss formula. |
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| large | ~0 | yes | Structural ceiling; reconsider the parameterization. |
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| large | large | no | Competing losses or optimizer inertia; isolate them. |
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| large | large | yes | The term helps but converges to the same basin; weak effect or coincidence. |
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## Structural-ceiling check, concretely
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```py
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# 1. Is d(loss)/d(metric) large? If so, the optimizer IS trying.
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metric = torch.tensor(0.5, requires_grad=True)
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loss = loss_fn(metric); loss.backward()
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print(metric.grad) # large (e.g. 350x the other grads) => it's trying
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# 2. Can the parameter change the metric? Trace loss -> metric -> intermediate -> parameter.
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# If d(metric)/d(parameter) ~ 0, the parameter structurally cannot move it.
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# (e.g. a V-rotation can't change the output basis when U is fixed.)
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# 3. Confirm empirically: set the term's coefficient to 0.
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# If the metric reaches the SAME value, it was never learned; it's structural.
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```
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