Topic
Adapting pretrained models to tasks and preferences.
Across 155 small-model experiments centered on Qwen 3.5 4B, the same thing kept working: give the model something executable it can check against the evidence it has, and it punches far above its benchmark weight. Here is the field guide — the levers that worked, how I know they're real, and the frontier they opened up.
TRL turns SFT, DPO, and GRPO into Trainer subclasses that inherit the entire Hugging Face stack — accelerate, peft, DeepSpeed. The convenience is real; the cost is that you're debugging someone else's training loop the moment your problem stops looking like the quickstart.
GRPO swaps PPO's learned critic for a Monte-Carlo baseline — the mean reward over a group of sampled completions — trading rollout compute and per-token credit assignment for a simpler, more stable RL loop on verifiable-reward tasks.