Standardizing Self-Evolution in AI Agents
As LLM agents move from static execution to autonomous improvement, the field lacks a unified framework for benchmarking how well these systems can 'self-evolve.' SEAGym addresses this by providing an evaluation environment specifically designed to test an agent's capacity to iterate on its own code, prompts, or strategies. The environment treats self-evolution as a continuous process, measuring not just the final output, but the efficacy of the agent's internal feedback loops and iterative refinement cycles.
Measuring Autonomous Improvement
SEAGym focuses on three core dimensions of agentic evolution:
- Feedback Integration: How effectively an agent incorporates external environment signals or internal performance metrics to adjust its behavior.
- Iterative Refinement: The ability of an agent to modify its own operational parameters (such as system prompts or tool-use logic) to increase success rates on subsequent attempts.
- Stability and Safety: Ensuring that as agents evolve, they do not degrade in performance or drift into unsafe operational states.
By providing a controlled sandbox, SEAGym allows researchers to compare different self-evolution strategies—such as reinforcement learning from AI feedback (RLAIF) versus direct prompt-based self-correction—in a consistent, reproducible manner. This is a critical step for moving beyond 'one-shot' agent performance toward systems that can adapt to novel tasks without human intervention.