{"root_cause":"The labels are probably fully masked (all set to the ignore index, usually -100), so the loss has no supervised tokens. A completion-only collator or chat-template boundary mismatch is the likely source.","confidence":0.94,"evidence":["Observation: training loss is exactly 0.000 from the first step.","Observation: every parameter gradient is zero.","Observation: generation remains unchanged.","Observation: input_ids and attention_mask look plausible, but labels are not logged.","Inference: the forward pass likely contains zero supervised target tokens; many causal-LM losses return zero when every label is ignored.","Inference: plausible prompt tokenization does not verify that the response span was found and left unmasked."],"competing_hypotheses":["The loss or logits were detached from the trainable parameters.","All trainable parameters are frozen or absent from the optimizer.","A loss weight is exactly zero."],"first_action":"Print one batch's labels, count labels != -100 per example, and decode the tokens at those supervised positions. This directly distinguishes an all-masked-label failure from optimizer or gradient-path failures.","prediction_if_true":"Every example will have zero labels != -100, or the intended assistant response tokens will be masked because the response-template boundary was not found.","prediction_if_false":"At least one intended response token will have a non--100 label; next inspect requires_grad, optimizer parameter membership, loss weighting, and whether logits or loss were detached.","immediate_changes":[],"fallback_logic":[]}