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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
Exercise answers
- Answer: Turn teacher trajectories into a TRL dataset
- Answer: Audit the set against the test split

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 2: Training data from traces

Step-by-step answer

Core 2–3 hours8 steps

Turn teacher trajectories into a TRL dataset

Back to the exercise

Previous answerCost the experiment and the deployment Next answer Audit the set against the test split

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