Title: Discussion 5 — Diagnosing a Stalled Fine-Tune
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 05 — Supervised Fine-Tuning Fundamentals with Hugging Face TRL

💬 Discussion 5 — Diagnosing a Stalled Fine-Tune (20 pts)

Imagine you're fine-tuning a model with TRL's SFTTrainer. The training loss is dropping steadily, exactly as you'd hope — but when you generate completions from the model at each checkpoint, the output quality isn't improving at all. It's flat: not worse, just not better. In a post of at least 150 words, answer:

- What diagnostics would you check first, and why — packing configuration, chat template application, or something else entirely?
- Walk through at least two concrete things you'd inspect in the data or training setup (for example: are prompt tokens being masked correctly, is the chat template being applied consistently between training and inference, are packed sequences bleeding context across example boundaries).
- What would each diagnostic outcome tell you about the root cause?

Then respond to at least two classmates, engaging with their specific diagnostic reasoning — agree, push back, or add a diagnostic they didn't mention.