{"slug": "debugging-an-llm-training-run", "title": "Debugging an LLM Training Run", "summary": "A debugging guide for large language model training runs recommends that engineers first test whether a model can overfit a tiny dataset of 100 examples, then check training and validation loss, data quality, training signals, and run ablations one change at a time. The guide notes that a low next-token prediction loss does not guarantee a model can follow instructions, call tools, return valid JSON, or make good decisions, and that aggregate accuracy such as 90% can mask failures on an important class. It advises comparing each single-variable change against a stable baseline and inspecting failing examples grouped into error types.", "body_md": "# Debugging an LLM Training Run\n\nLLM training has more moving parts than classic machine learning, but the debugging method is similar. Engineers check the training pipeline, data, objective, optimization, and evaluation in that order.\n\nThe fastest first test is to overfit a tiny dataset.\n\nTake 100 examples and train on them many times. The model should almost memorize them. If it cannot, the problem is probably not data coverage. The problem may be the loss function, masking, tokenizer, optimizer, gradient flow, or training code.\n\nAfter that, look at training and validation loss.\n\nIf training loss does not go down, the model is not learning. If training loss is good but validation loss is bad, the model may be overfitting. If both losses look good but the real output is still bad, the training objective may not match the behavior you want.\n\nThis last case is common in LLM training.\n\nA low next-token prediction loss does not mean the model can follow instructions, call tools, return valid JSON, or make good decisions. The model may be learning exactly what the loss asks for while still failing the real task.\n\nData is another major source of problems. Engineers check bad samples, duplicate data, wrong labels, data mixture, missing cases, and train-test distribution.\n\nAggregate accuracy is often not enough. A model with 90% accuracy may still fail on one important class. A confusion matrix can show where the failures are. Engineers then inspect those examples and group them into error types.\n\nTraining signals also help find lower-level problems. Engineers watch learning rate, gradient norm, clipping, NaN values, token distribution, sequence length, and batch statistics. A sudden gradient spike may point to a bad batch.\n\nNext is ablation. Change one thing at a time. Remove one dataset. Change one data mixture. Remove synthetic data. Change the learning rate. Compare the result against a stable baseline.\n\nHere is a practical debugging process:\n\n- Can it memorize a tiny dataset?\n- Is training loss healthy?\n- Is validation loss healthy?\n- Which classes fail?\n- Which examples fail?\n- Is the label or objective wrong?\n- Change one thing and test again.", "url": "https://wpnews.pro/news/debugging-an-llm-training-run", "canonical_source": "https://julin.ai/2026/09/20/debug-llm-model/", "published_at": "2026-09-19 12:00:00+00:00", "updated_at": "2026-09-20 10:25:11.050050+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "ai-research", "mlops"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/debugging-an-llm-training-run", "markdown": "https://wpnews.pro/news/debugging-an-llm-training-run.md", "text": "https://wpnews.pro/news/debugging-an-llm-training-run.txt", "jsonld": "https://wpnews.pro/news/debugging-an-llm-training-run.jsonld"}}