The Five Stages of Reinforcement Learning

2026-06-16 · Source: Alex Strick van Linschoten · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

The article outlines the five stages of a reinforcement learning (RL) setup for LLM agents: Tasks, Harness, Rollout, Reward, and Trainer. It details how "Tasks" define problems, "Harness" provides tools, "Rollout" records solution attempts, "Reward" scores these attempts, and "Trainer" adjusts model weights using algorithms like GRPO, as of June 2026. The combination of tasks, harness, and rewards forms the "environment." Unlike supervised fine-tuning, RL tasks focus on scoring attempts rather than copying target answers. The author emphasizes the critical role of reward design, noting a shift in 2025-2026 towards sparse, outcome-based rewards over dense, step-by-step scoring, even for multi-part evaluations. The piece also mentions memory efficiency improvements, such as Unsloth allowing GRPO to fit in approximately 5GB of VRAM.

Key takeaway

For AI Scientists or Machine Learning Engineers developing LLM agents, understanding the five stages of RL is crucial. Focus your efforts on sophisticated reward design, as it's the primary lever for agent improvement, especially with the shift towards sparse, outcome-based scoring. Consider how your domain expertise can inform nuanced reward functions, moving beyond simple correctness checks. This approach will significantly impact your agent's ability to learn and perform complex tasks effectively.

Key insights

Reinforcement learning for LLM agents involves five distinct stages, with reward design being a critical, nuanced component.

Principles

• RL tasks score attempts, not copy answers.
• "Environment" combines tasks, harness, and rewards.
• Sparse outcome rewards are increasingly preferred.

Method

An RL process for LLM agents involves defining tasks, providing tools (harness), recording solution attempts (rollout), scoring these attempts (reward), and updating model weights via algorithms like GRPO (trainer).

In practice

• Decompose RL into Tasks, Harness, Rollout, Reward, Trainer.
• Design multi-part, outcome-based reward functions.
• Consider Unsloth for GRPO memory efficiency.

Topics

• Reinforcement Learning
• LLM Agents
• Reward Design
• GRPO Algorithm
• Structured Extraction
• Unsloth Framework

Best for: AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Alex Strick van Linschoten.