finetune-hyperparameter-sweep

name

finetune-hyperparameter-sweep

description

Fine-tuning LR protocol — full FT, LoRA / QLoRA / DoRA / PEFT adapter, across SFT, DPO, and GRPO / PPO / RL objectives. Fires on ANY fine-tune, including (especially) when the plan already fixes a learning rate or copies one from a reference paper: a fixed LR is one grid point, never the answer, so LR is always swept over a wide range. The only test of an LR is whether the run hits the pass criteria in `task.md` or the milestone's own claim criteria — loss, grad norm, and reward curves are never acceptance evidence, no matter how good they look. Criteria not met = change LR and re-run. Triggers: `learning_rate`, `SFTTrainer`, `DPOTrainer`, `GRPOTrainer`, `LoraConfig`, `peft_config`, `full fine-tune`, `SFT`, `DPO`, `GRPO`, `RLHF`, `QLoRA`, `DoRA`, `PEFT`, `adapter`.

Fine-Tuning LR Sweep

1. Any fine-tune → always sweep LR over a wide range. Whether the plan hard-codes an LR, copies one from a paper, or leaves it open makes no difference: that value is one grid point, never the answer.

2. The only test of an LR is the criteria — the pass criteria in task.md, or the claim criteria of the relevant experiment-plan milestone. Nothing else counts as acceptance evidence: a converged loss, a healthy grad norm, a rising reward, a "reasonable-looking" curve certify nothing. Criteria not met = LR not acceptable, no matter how good the training curves look.

3. Criteria not met → change LR and re-run. Keep going until an LR passes. Only after the whole LR range has failed should you touch anything else.