Tag
This paper proposes EffRank/n and D_act as low-overhead diagnostics to measure effects of reward attribution in cooperative multi-agent RL, and tests on SMACv2, finding that observation explains geometry while reward attribution mainly affects behavior.
Introduces delayed per-step reward attribution with eligibility gating for reinforcement learning in multi-agent language model interactions, achieving first place in the MindGames Arena benchmark at NeurIPS 2025.