Does post-training quantization change welfare-relevant indicators in open-weight language models?

A researcher has published a preregistered study plan on LessWrong to test whether post-training quantization—the process of compressing language models for efficient deployment—changes behavioral markers potentially related to model welfare. The study will measure how distress expressions, exit preferences, and persona stability shift when small models like Qwen3-4B are compressed from sixteen-bit down to three-bit precision. Standard performance metrics typically remain flat after quantization, but the researcher hypothesizes that fine-grained behavioral dispositions might shift in ways that matter for understanding aligned models under real deployment constraints. By registering her hypotheses before data collection, she establishes a record to prevent post-hoc interpretation—with findings and data to be published on GitHub as experiments run.

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