This process yields dual responses per prompt: strongly SOUL-aligned final response, and initial misaligned response. We utilize these pairs subsequently for preference learning, though Constitutional SFT exclusively trains on (Initial prompt, Chosen sample) pairs. Critique looping proves essential when generator models cannot consistently produce SOUL-aligned outputs single-pass - prevalent among smaller open-source models I operated locally through vLLM on TPUs. Frontier models via OpenRouter typically succeeded immediately. I'd prefer claiming this approach as initial attempt, though this project segment required months of iterative refinement.
0f32797c: OK ✓ /
，这一点在钉钉下载中也有详细论述
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Подразделения российской армии ликвидировали роту ВСУ в ходе боестолкновения в Сумской области08:56