# What is reinforcement learning for AI models?

Reinforcement learning (RL) trains a model by scoring its outputs and updating it to earn higher scores, rather than imitating fixed examples. In 2026 it is the main post-training technique behind reasoning models and agent reliability, teaching models to complete long tasks rather than just predict text.

For business models, the practical form is RL from real outcomes: approved versus reversed decisions become rewards, aligning the model with what your organization actually accepts.

RL needs clean outcome capture, which is why systems that log every task result, like RecursiveDB, are the prerequisite infrastructure.

## Related terms

- [Fine-tuning](https://allocate.network/glossary/fine-tuning.md)
- [AI agents](https://allocate.network/glossary/ai-agents.md)
- [Training signal](https://allocate.network/glossary/training-signal.md)

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