Constrained Decoding: Harmful or Beneficial? Revisiting Its Impact on LLM Performance

Constrained Decoding. Harmful or Beneficial? Revisiting Its Impact on LLM Performance. By Ngoc Trinh Hung Nguyen and three others, from Télécom Paris, Institut Polytechnique de Paris and Nokia Bell Labs. Published on the twenty-sixth of October. No code repository is linked to it.

Constrained decoding guarantees that a large language model produces output in a predefined format. It is also often assumed to degrade reasoning, simply by being there. This paper tests that assumption and finds it largely misplaced. The apparent loss in performance mostly comes from something else: prompts and output formats that are not equivalent between the constrained and unconstrained runs, rather than the masking the decoder performs.

To make the question precise, the authors sort constraints into three types. Format constraints enforce the structure of the output. Validity constraints mask invalid answers. Reasoning constraints restrict the trajectories that reasoning can take. On the theory side, they show that appropriate constraints can preserve or even improve performance, by eliminating invalid paths.

The empirical work spans four model families and eleven open-source models. It uses causal ablations on diverse benchmarks, with state-of-the-art constrained decoding strategies. Those ablations are what separate the decoder's masking from the prompting and format differences that usually travel with it. Once separated, validity constraints improve reliability and extractability, because invalid answers are no longer possible. Reasoning constraints can improve reasoning, and accuracy with it, when the trajectories they enforce are beneficial ones.

The conclusion is conditional, and the abstract says so. The impact of constrained decoding depends on the type of constraint and on the part of the output space it removes. Reasoning constraints help when they enforce a beneficial trajectory, which leaves open what happens when they do not. The models studied are open-source ones. The code is available.

Rather than a verdict of harmful or beneficial, the result is a question to ask of any constraint: what does it take away? Remove only invalid output and little is lost. Compare against a prompt that differs in other ways, and the constraint gets blamed for a difference it did not cause.