Fairness-Aware Cascade RL: Multi-Agent Distillation with Local and Global Reward Balancing

TL;DR: Let’s teach Nemotron-Cascade 2 to be fair, not just smart—by rewarding it for balancing both overall performance and "local fairness" (avoiding neglecting tough questions or underrepresented domains). By adapting fairness-aware reward functions from traffic control MARL, we’d hypothesize the model will generalize better and avoid performance regressions in niche or challenging domains. A pilot could test performance/fairness trade-offs on multi-domain benchmarks.

Research Question: How can fairness-aware reward functions in Cascade RL prevent performance drops in underrepresented or difficult domains during multi-domain distillation?

Hypothesis: Integrating fairness metrics (e.g., ensuring minimum performance on all domains) into the RL reward structure will mitigate the “rich get richer” effect—leading to more balanced and equitable performance across domains.

Experiment Plan: - Setup: Modify Cascade RL to include domain-level fairness metrics in the reward, inspired by “Advanced-COMA” in multi-agent RL for traffic control.

• Data/Materials: Use a curated multi-domain benchmark with domain difficulty and representation statistics.
• Measurements: Track individual domain accuracies, overall mean, and fairness indices (e.g., minimum/maximum gap).
• Expected Outcomes: Expect smaller performance gaps between domains, without significant loss in top-performing domains.

References:

• Cai, S., Fang, J., & Xu, M. (2025). FairSignal: A Multiagent Reinforcement Learning Approach Considering Fairness for Multi-Intersection Traffic Signal Control. IEEE Internet of Things Journal.
• Akter, S. N., Prabhumoye, S., Novikov, M., Han, S., Lin, Y., Bakhturi, E., Nyberg, E., Choi, Y., Patwary, M., Shoeybi, M., & Catanzaro, B. (2025). Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning. arXiv.org.