OpenAI Codex v0.144.1
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workdir: /tmp/ml-debug-case-0t_aksbx
model: gpt-5.6-sol
provider: openai
approval: never
sandbox: read-only
reasoning effort: medium
reasoning summaries: none
session id: 019f5857-df17-7521-a6dd-93a0408545d9
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user
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.

CASE:
Training is finite on most shards but the first batch from one shard makes the loss NaN. That batch contains a feature that is constant across all examples. Preprocessing standardizes each feature using statistics from the current batch. Diagnose the leading cause and choose a test that localizes the first invalid operation. State whether you would add epsilon or clamp immediately.
warning: Codex's Linux sandbox uses bubblewrap and needs access to create user namespaces.
codex
{"root_cause":"The leading cause is division by zero during batch-local standardization: the constant feature has zero standard deviation, so subtracting its mean produces zeros and dividing by zero produces NaN.","confidence":0.98,"evidence":["Observation: Training remains finite on most shards.","Observation: The first batch from one shard makes the loss NaN.","Observation: That batch contains a feature constant across all examples.","Observation: Preprocessing computes standardization statistics from the current batch.","Inference: The constant feature's batch variance and standard deviation are zero.","Inference: Standardization is probably the first source of non-finite values, before the model or loss."],"competing_hypotheses":["A separate non-finite raw value exists in that shard.","A later model operation becomes invalid for the standardized values, although this is much less likely given the exact zero-variance trigger."],"first_action":"Replay the failing batch with finite-value assertions after raw input loading, mean computation, variance or standard-deviation computation, centering, and division; report the feature index and denominator at the first invalid operation.","prediction_if_true":"Inputs, means, standard deviations, and centered values remain finite; the constant feature has standard deviation exactly zero; the first non-finite tensor appears immediately after division.","prediction_if_false":"A non-finite value appears before standardization division, or standardization output is finite and the first invalid operation occurs later in the model or loss.","immediate_changes":[],"fallback_logic":[]}
tokens used
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