Integrating a competitive 'Red Team' evolutionary branch into GenEnv, which specifically optimizes for environment configurations that trigger agent failure modes, will result in agents with significantly higher Out-of-Distribution (OOD) robustness compared to purely difficulty-aligned curricula.

Motivation

While GenEnv focuses on difficulty alignment for skill acquisition (a cooperative teacher-student dynamic), it may overlook 'blind spots' or adversarial vulnerabilities. Introducing an adversarial dynamic mimics the robustness benefits of Generative Adversarial Networks (GANs), ensuring agents are not just capable but also resilient to edge cases and prompt injections.

Proposed Method

Extend the GenEnv framework to a three-player game: the Agent, the Instructor (difficulty-aligned), and the Adversary (failure-inducing). The Adversary evolves by generating environments that maximize the Agent's error rate while maintaining solvability (verified by a stronger oracle or the Instructor). Train the Agent to satisfy both the Instructor's curriculum and the Adversary's edge cases. Evaluate on safety benchmarks (e.g., Do Not Answer) and robustness datasets (e.g., AdvGLUE).

Expected Contribution

A framework for 'Adversarial Co-Evolution' that produces agents that are both capable and safety-aligned without requiring manual red-teaming datasets.

Required Resources

High-end GPU cluster for concurrent LLM inference (3 models interacting), access to safety/robustness benchmarks, and an oracle verifier (e.g., GPT-4) to ensure adversarial tasks remain solvable.

Source Paper

GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators

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