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Coders In Hoods

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Course overview

Fine-Tuning and RL for Agents

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1 · Decide and prepare

1Prompt, optimise or train?
Exercise answers
- Answer: Measure the baseline to beat
- Answer: Cost the experiment and the deployment
2Training data from traces

2 · Supervised and preference fine-tuning

3SFT with LoRA on a free GPU
4Preference optimisation

3 · Reinforcement learning for agents

5GRPO in practice
6Designing rewards
7Multi-turn RL environments
8Reward hacking, regressions and shipping

CapstoneTrain a triage model, then decide whether to ship it Reference implementation

Answers Lesson 1: Prompt, optimise or train?

Step-by-step answer

Stretch 45 min7 steps

Cost the experiment and the deployment

Back to the exercise

Previous answerMeasure the baseline to beat Next answer Turn teacher trajectories into a TRL dataset

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