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LLM agents achieve substantial performance gains by jointly learning credit at both the turn and token generation levels, overcoming limitations of existing single-level credit assignment methods.
Scaling zero RL to a trillion parameters reveals that models can spontaneously develop advanced cognitive behaviors, making traditional heuristics obsolete.
Jointly modeling need prediction and service recommendation in an LLM framework dramatically improves local life service recommendations, outperforming traditional isolated approaches.