AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents

Problem
The paper addresses the lack of rigorous evaluation frameworks for continual learning in language agents, particularly in the context of task reuse and memory design. Existing benchmarks primarily focus on retrieval and reasoning tasks, often employing naive task streams that do not adequately analyze cross-task relationships. This gap hinders the understanding of how agents can accumulate and reuse knowledge over time. The authors note that this work is a preprint and has not undergone peer review.

Method
The authors propose AGENTCL, an evaluation framework that constructs controlled task streams designed to facilitate the reuse of earlier solutions, evidence, or workflows in subsequent tasks. This contrasts with naive task streams, where such reusability is not guaranteed. The framework includes metrics for assessing transfer gains, allowing for a more nuanced evaluation of continual learning capabilities. To further investigate the impact of memory design on continual learning, the authors introduce MemProbe, a probing method that captures interactions, insights, and skills while filtering out unreliable experiences during the consolidation phase. The empirical evaluation encompasses various tasks, including coding, deep research, and language understanding/reasoning, to assess the effectiveness of different memory designs.

Results
The empirical analysis reveals that naive task streams provide limited differentiation in evaluating memory designs, while controlled task streams yield clearer distinctions in terms of plasticity and performance. Specifically, the results indicate that agents utilizing controlled streams demonstrate improved transfer gains and reduced memory-induced degradation compared to those using naive streams. The paper does not provide specific numerical results or effect sizes against named baselines, focusing instead on qualitative insights into the performance of memory designs.

Limitations
The authors acknowledge that their framework is still in the early stages and may require further refinement to fully capture the complexities of continual learning in language agents. They also note that the reliance on controlled task streams may limit the generalizability of findings to more dynamic, real-world scenarios. Additionally, the paper does not explore the scalability of the proposed methods or their applicability to a broader range of tasks beyond those tested.

Why it matters
The introduction of AGENTCL has significant implications for the field of continual learning in language agents, as it provides a structured approach to evaluate and improve memory designs. By emphasizing the importance of task reusability and the impact of memory architecture, this work lays the groundwork for future research aimed at enhancing the efficiency and effectiveness of language agents in real-world applications. The findings underscore the necessity for stronger memory designs that balance plasticity with stable knowledge reuse, which is crucial for developing more robust AI systems. This work is available on arXiv.