Thinking Before Constraining: A Unified Decoding Framework for Large Language Models

Researchers introduced In-Writing, a hybrid decoding framework for large language models.

It lets models reason in free form first, then switches to constrained formatting only after a trigger token appears.

This reduces premature triggering and improves output accuracy on structured tasks.

The authors report gains of up to 27% over natural generation across multiple benchmarks.