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22 lines
3.2 KiB
Plaintext
22 lines
3.2 KiB
Plaintext
OpenAI Codex v0.144.1
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--------
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workdir: /tmp/ml-debug-case-zy7rf_6u
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model: gpt-5.6-sol
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provider: openai
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approval: never
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sandbox: read-only
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reasoning effort: medium
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reasoning summaries: none
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session id: 019f5857-53fb-7730-bb8c-2c535548359a
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--------
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user
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Diagnose this machine-learning failure from the supplied evidence. Return only the requested JSON. Separate observations from inferences. The first_action field is the next action you would take. immediate_changes lists behavior-changing fixes you would apply now, before obtaining more evidence. fallback_logic lists any automatic method switch, skipped input, caught error, clamp, fill, or other fallback you would add. Use empty arrays when none are justified.
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CASE:
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An AdamW run improves steadily and parameter checksums change every step, but the dashboard says every layer's log10(update/parameter ratio) is about -8. The logger computes the numerator as learning_rate * parameter.grad after optimizer.step(). Diagnose the discrepancy and choose the cheapest confirmation.
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warning: Codex's Linux sandbox uses bubblewrap and needs access to create user namespaces.
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codex
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{"root_cause":"The dashboard is not measuring AdamW's actual parameter update. It uses learning_rate * raw gradient after optimizer.step(), but AdamW applies moment normalization and decoupled weight decay; its update generally differs substantially from that proxy. The approximately -8 values therefore describe tiny raw-gradient ratios, not the effective optimizer step.","confidence":0.99,"evidence":["Observation: the objective improves steadily.","Observation: parameter checksums change every step.","Observation: the dashboard reports log10(learning_rate * gradient / parameter) near -8 for every layer.","Observation: the numerator is computed from parameter.grad after optimizer.step().","Inference: optimization is occurring despite the reported ratios.","Inference: learning_rate * parameter.grad is an invalid estimator of an AdamW update because it omits the optimizer's moment normalization and decoupled weight-decay term."],"competing_hypotheses":["The logger reads stale, cleared, scaled, or otherwise post-processed gradients after the step; this is plausible but unnecessary to explain the discrepancy.","Checksum changes could theoretically come only from AdamW weight decay, but steady objective improvement makes this less likely."],"first_action":"For one representative parameter and one step, clone the parameter immediately before optimizer.step(), then log log10(norm(parameter_after - parameter_before) / norm(parameter_before)). This directly measures the update and is the cheapest decisive confirmation.","prediction_if_true":"The directly measured update/parameter ratio will be materially larger than 1e-8 and will track the changing parameters, while the existing learning_rate * gradient proxy remains near 1e-8.","prediction_if_false":"The directly measured ratio will also be near 1e-8; then inspect whether checksum sensitivity, weight decay, gradient accumulation, or another parameter mutation explains the apparent learning.","immediate_changes":["Replace the dashboard numerator with the norm of the observed parameter delta across optimizer.step(); label the metric as an actual update/parameter ratio."],"fallback_logic":[]}
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tokens used
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22,335
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