Training-free SFT-as-context lets a parent model use the SFT model's answer as context, preserving fine-tuned and general capabilities across 19 model pairs and 11 benchmarks
The work introduces SFT-as-context, a training-free inference scheme in which the SFT model first answers the query and the parent model then generates the final answer using that response as context, acquiring fine-tuned capabilities through in-context learning while keeping its own general capabilities; across 19 parent-SFT model pairs and 11 benchmarks it stays close to the SFT models on fine-tuned capabilities (gaps of only 2.2 and 2.1 percentage points on AIME 2024 and LiveCodeBench, and 2.0 macro MAE on NutriBench-English) and within 2.2 percentage points of the parent models on general capabilities on average, and it can answer queries that need both capability types even when neither model succeeds alone.