My first RL environment: three stages, no trainer

2026-06-20 · Source: Alex Strick van Linschoten · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

The article details a practitioner's first hands-on experience building a Reinforcement Learning (RL) environment for structured data extraction. It explores three methods for processing training traces: filtering, which discards data below a reward threshold (e.g., ">=1.0"); weighting, which scales the training signal by reward, including a softmax variant where exp(0)=1 ensures even 0.0 reward traces retain a ~0.13 weight; and full RL, which uniquely allows for negative gradients to push away from bad behavior. The author then demonstrates constructing a basic RL environment using the "verifiers" library, specifically "vf.SingleTurnEnv", by integrating a dataset and a custom "extraction_reward" function within a "vf.Rubric". This setup, exemplified with ISAF press release data, focuses solely on environment definition, deliberately omitting a trainer.

Key takeaway

For AI Engineers or ML practitioners building structured data extraction agents, understanding how to shape training traces is crucial. Filtering can simplify data, but weighting with softmax offers nuanced signal control, while full RL provides essential negative feedback. Consider using "verifiers" to rapidly prototype your RL environments and experiment with custom reward functions and softmax temperature to optimize agent learning.

Key insights

RL environments bridge labeled data to reward-based learning, requiring careful trace shaping.

Principles

• Filtering risks data loss and ignores negative signal.
• Reward weighting scales learning, but softmax retains some signal from zero-reward traces.
• Full RL uniquely allows negative gradients to actively push away undesirable behavior.

Method

Build an RL environment using "verifiers" by defining a "Dataset" for tasks and wrapping a custom reward function (e.g., "extraction_reward") in a "vf.Rubric", then instantiating "vf.SingleTurnEnv" with these components.

In practice

• Use "verifiers" to define RL environments.
• Implement custom reward functions for structured extraction.
• Explore softmax temperature to adjust reward signal emphasis.

Topics

• Reinforcement Learning
• Structured Data Extraction
• Reward Shaping
• Softmax Temperature
• verifiers Library
• LLM Agents

Best for: Machine Learning Engineer, AI Engineer, AI Student

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