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Past post-training successes often turn toxic when reapplied to drifted checkpoints, making pre-training experience authorization essential to prevent autonomous self-improvement loops from wasting compute and destabilizing model weights.
ADaPT enables a single model to flexibly navigate the efficiency-performance trade-off, achieving significant cost savings without sacrificing reasoning quality.
