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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

3 · Reinforcement learning for agents

5GRPO in practice
Exercise answers
- Answer: GRPO the verifiable triage fields on a free GPU
- Answer: Reproduce TRL's loss aggregations by hand
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 5: GRPO in practice

Step-by-step answer

Stretch 45–60 min6 steps

Reproduce TRL's loss aggregations by hand

Back to the exercise

Previous answerGRPO the verifiable triage fields on a free GPU Next answer Build and validate a composite triage reward

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