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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?
2Training data from traces

2 · Supervised and preference fine-tuning

3SFT with LoRA on a free GPU
4Preference optimisation
Exercise answers
- Answer: DPO the free-text fields with on-policy pairs
- Answer: KTO from thumbs, against DPO on the same budget

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 4: Preference optimisation

Step-by-step answer

Stretch 3 h (about 1.5 h on the GPU)5 steps

KTO from thumbs, against DPO on the same budget

Back to the exercise

Previous answerDPO the free-text fields with on-policy pairs Next answer GRPO the verifiable triage fields on a free GPU

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