Are Self-Evolving Agents Continual Learners?

The paper assesses three self-evolving methods (ReasoningBank, GEPA, and Meta-Harness) to determine if they can continually learn new tasks while preserving previously learned skills, using metrics like terminal advantage and average advantage. GEPA achieves the highest plasticity but incurs the most severe stability loss, indicating a trade-off between adapting to new tasks and retaining previous knowledge The study shows that self-evolution can improve performance, but current methods fail to balance plasticity and stability effectively, leading to significant interference and forgetting. Find the latest research on continual learning in self-evolving agents? Research question and conceptual framework “Are Self-Evolving Agents Continual Learners?” examines whether nonparametric self-evolving LLM agents satisfy the central requirement of continual learning: acquiring competence on incoming task families while preserving performance on previously learned and not-yet-seen families (2610.25858). The paper focuses on agents whose model parameters remain frozen and whose adaptation occurs exclusively through the harness: system prompts, external memories, skill files, and controller code. This formulation is important because self-evolution operates at the timescale of deployment. Unlike gradient-based continual learning, which requires parameter updates and often substantial data or computation, harness-level adaptation can occur after individual episodes. The paper formalizes an evolver ff that maps the current harness and accumulated experience to a new harness. The resulting agent policy is therefore determined by the fixed LLM and a changing nonparametric state. The authors distinguish two adaptation paradigms. Self-modification rewrites globally shared artifacts, as in GEPA and Meta-Harness. Experience augmentation stores reflections or trajectories externally and retrieves them at inference time, as in ReasoningBank. Both mechanisms can improve performance on the task that generated the update, but they expose different interference mechanisms when the task distribution changes. The study defines continual learning over task families and evaluates the complete policy sequence rather than only the final checkpoint. This is a consequential methodological choice: a method that eventually relearns a task after damaging it may obtain a reasonable terminal score while performing poorly over much of the deployment horizon. Performance is therefore …

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