Beyond Endpoint Gains: A Weight-Delta Audit of Medical Specialization
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TL;DR - This paper audits the weight changes between general-purpose LLMs and their medical-specialized counterparts, finding that decoder updates closely reproduce benchmark gains but do not yield a simple component-level explanation of specialization.
- Examines aligned Gemma-3-to-MedGemma and Qwen2.5-to-HuatuoGPT-o1 checkpoint pairs.
- Full decoder deltas strongly reconstruct medical benchmark movement, with endpoint-normalized retention of 0.974 and 1.183.
- MLP layers are the strongest broad component family in both pairs, but controls and rollback tests prevent uniquely attributing the gains to them.
- Conclusions are limited to text-only multiple-choice benchmarks and do not establish clinical validity or circuit-level mechanisms.
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Beyond Endpoint Gains: A Weight-Delta Audit of Medical Specialization
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TL;DR - This paper audits the weight changes between general-purpose LLMs and their medical-specialized counterparts, finding that decoder updates closely reproduce benchmark gains but do not yield a simple component-level explanation of specialization.
- Examines aligned Gemma-3-to-MedGemma and Qwen2.5-to-HuatuoGPT-o1 checkpoint pairs.
- Full decoder deltas strongly reconstruct medical benchmark movement, with endpoint-normalized retention of 0.974 and 1.183.
- MLP layers are the strongest broad component family in both pairs, but controls and rollback tests prevent uniquely attributing the gains to them.
- Conclusions are limited to text-only multiple-choice benchmarks and do not establish clinical validity or circuit-level mechanisms.