{"root_cause":"The labels are almost certainly fully masked (every label is -100), so the loss function has no supervised tokens and produces zero loss and zero gradients.","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: Logs show input_ids and attention_mask, but do not show labels.","Observation: The decoded input looks plausible, which checks tokenization but not supervision masking.","Inference: The joint pattern is characteristic of all labels being set to the ignore index (-100), commonly due to faulty response-boundary or completion-only masking."],"competing_hypotheses":["The loss is accidentally multiplied by zero or replaced with a detached constant.","The logits or loss are detached from the model computation graph.","The logged loss is incorrect, though this would not alone explain zero gradients and unchanged generation."],"first_action":"Print one real batch's decoded input alongside labels, then assert `(labels != -100).sum() > 0` before the forward pass. This is the cheapest test because it directly distinguishes missing supervision from downstream loss or autograd bugs.","prediction_if_true":"Every label will be -100, the supervised-token count will be zero, and the assertion will fail before training.","prediction_if_false":"At least one label will contain a real token ID; next inspect the unreduced per-token loss and its grad_fn for zero scaling or detachment.","immediate_changes":[],"fallback_logic":[]}
