Jingdong (China)

GRPO scores every rollout of a prompt against one shared group-mean baseline, yet rollouts of the same prompt often follow different solution strategies with different reward distributions. This work defines the subgroup offset as a subgroup mean's deviation from the global mean and shifts the baseline along it by a single signed coefficient: the positive sign pulls credit toward a rollout's standing inside its subgroup, the negative sign pulls it toward the subgroup's overall quality. On Qwen3-8B-Base and Olmo3-7B-Base across four competition-level math benchmarks, both directions give modest but consistent gains over an anchor that shares the same strategy-conditioned sampling and differs only in the baseline, concentrated in pass@16, with the negative direction stronger.

GRPO scores every rollout of a prompt against one shared group-mean baseline, yet rollouts of the same prompt often follow different solution strategies with different reward distributions. This work defines the subgroup offset as a subgroup mean's deviation from the global mean and shifts the baseline along it by a single signed coefficient: the positive sign pulls credit toward a rollout's standing inside its subgroup, the negative sign pulls it toward the subgroup's overall quality. On Qwen3-8B-Base and Olmo3-7B-Base across four competition-level math benchmarks, both directions give modest but consistent gains over an anchor that shares the same strategy-conditioned sampling and differs only in the baseline, concentrated in pass@16, with the negative direction stronger.

Medical large language models answer single encounters well but struggle to accumulate and govern experience across encounters. HealthJev separates shared safety rules and medical knowledge from a private longitudinal memory, and a self-trained lightweight decision model, JevGate, screens candidate lessons before they enter memory at a median 86 ms per decision. On a longitudinal benchmark of 90 simulated daily interactions for 20 users built from real MIMIC-IV trajectories, it scored a mean 0.668, versus 0.634 for MemoryBank, 0.593 for Mem0, 0.510 for ReAct and 0.502 for Memp; across six biomedical tasks it gained 13.3 percentage points on average over the base condition.

Medical large language models answer single encounters well but struggle to accumulate and govern experience across encounters. HealthJev separates shared safety rules and medical knowledge from a private longitudinal memory, and a self-trained lightweight decision model, JevGate, screens candidate lessons before they enter memory at a median 86 ms per decision. On a longitudinal benchmark of 90 simulated daily interactions for 20 users built from real MIMIC-IV trajectories, it scored a mean 0.668, versus 0.634 for MemoryBank, 0.593 for Mem0, 0.510 for ReAct and 0.502 for Memp; across six biomedical tasks it gained 13.3 percentage points on average over the base condition.