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[2602.21515] Training Generalizable Collaborative Agents via Strategic Risk Aversion
Summary
This paper explores training strategies for collaborative agents, emphasizing strategic risk aversion to enhance generalizability and robustness in multi-agent systems.
Why It Matters
As collaborative AI systems become more prevalent, ensuring their ability to work effectively with diverse partners is crucial. This research addresses common pitfalls in agent collaboration, offering a novel approach that could improve the reliability and performance of AI in real-world applications.
Key Takeaways
- Strategic risk aversion can enhance collaboration among agents.
- The proposed method leads to better equilibrium outcomes than traditional game theory.
- Empirical results show improved performance in heterogeneous partner scenarios.
Computer Science > Machine Learning arXiv:2602.21515 (cs) [Submitted on 25 Feb 2026] Title:Training Generalizable Collaborative Agents via Strategic Risk Aversion Authors:Chengrui Qu, Yizhou Zhang, Nicholas Lanzetti, Eric Mazumdar View a PDF of the paper titled Training Generalizable Collaborative Agents via Strategic Risk Aversion, by Chengrui Qu and 3 other authors View PDF HTML (experimental) Abstract:Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals. Unfortunately, existing approaches to learning policies for such collaborative problems produce brittle solutions that fail when paired with new partners. We attribute these failures to a combination of free-riding during training and a lack of strategic robustness. To address these problems, we study the concept of strategic risk aversion and interpret it as a principled inductive bias for generalizable cooperation with unseen partners. While strategically risk-averse players are robust to deviations in their partner's behavior by design, we show that, in collaborative games, they also (1) can have better equilibrium outcomes than those at classical game-theoretic concepts like Nash, and (2) exhibit less or no free-riding. Inspired by these insights, we develop a multi-agent reinforcement learning (MARL) algorithm that integrates strategic risk aversion into standard policy optimization methods. Our empirical results across collaborative benchmarks (includin...
