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A self-evolving memory paradigm for generative recommendation is proposed, aiming to enable effective evolution across heterogeneous behavioral patterns, and three key principles for effective self-evolving recommendation systems are identified, including isolated memorization, reinforced evolution, and scalable application.
Switch Distillation not only enhances reasoning performance by up to 71% but also preserves factual recall, challenging the conventional trade-off in knowledge distillation.
Long-horizon LLM agents can achieve 96.9% task success by learning to adapt their external execution support through trainable harness policies.
G2Rec captures user interest prototypes more accurately than existing methods, enabling generative recommendation systems to operate without ground-truth user interests.
Achieve significant reasoning gains in frozen LLMs (+22.4%) without retraining by adaptively routing reward model guidance at the token level during inference.
