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GMAE aggregates reward realizations across possible contexts into a response-level distribution and estimates expected advantages, which exhibits stable learning, low extra cost, and good applicability across training datasets and RL backbones.
AdaMem is introduced, which uses adaptive natural-language Memory Policies to personalize what an agent writes to memory and demonstrates the promise of adaptive write control while exposing policy execution as a central limitation of current memory agents.
This work provides a scalable and effective framework for extending RLVR beyond the limitations of pattern-based verification to complex, noisy, real-world domains, and generalizes strongly to seven out-of-distribution benchmarks.
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