Practical RL Today: Where It's Actually Used in AI Engineering
~11 min read
Beyond theory, RL shows up in specific, identifiable places in modern AI engineering — model alignment, reasoning fine-tuning, agent training, and recommendation/ranking systems — each already covered elsewhere in this curriculum.
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Key points
- •Model alignment via RLHF is RL's biggest real-world deployment by user impact — every major chat model goes through an RLHF-style stage
- •Reasoning fine-tuning via GRPO is the newer, fast-growing application, targeting verifiable tasks (math/logic/code) — covered in sft-vs-rft and grpo-reasoning
- •Agentic training (the ART framework) applies GRPO at the trajectory level to train multi-step tool use and planning, not single-turn text quality
- •Beyond LLMs, RL has a long track record in recommendation/ranking (optimizing long-term engagement via the value-function idea), robotics, and game-playing systems
- •In practice, most AI engineers apply RL indirectly through fine-tuning frameworks (GRPO/TRL) or by using already-RLHF-tuned models, rather than implementing Q-learning/PPO from scratch