SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
SEAGym creates a dynamic evaluation environment for self-evolving LLM agents, comparing ACE, TF-GRPO, and AHE across multiple metrics on Terminal-Bench 2.0 and HLE.
Key Findings
Methodology
SEAGym adopts an RL-style environment formulation, transforming static benchmarks into reusable task sources with train batches, frozen validation, transfer views, replay diagnostics, and snapshot records. It supports flexible scheduling parameters for single-task, online, and epoch-based evolution, enabling comprehensive analysis of model performance, stability, and cost. The environment records snapshots and metrics at explicit evaluation points, facilitating multi-angle assessment of the self-evolution process, emphasizing the influence of snapshot quality, source diversity, and model backend on robustness.
Key Results
- On Terminal-Bench 2.0 and HLE, AHE outperforms others in validation, ID, and OOD transfer, with validation gains up to 17.1 percentage points. However, frequent updates do not always lead to persistent improvements; some intermediate snapshots regress or collapse. Batch size impacts performance, with batch 20 providing the best balance. Source diversity experiments show combining HLE sources enhances transfer, while single-source limits generalization. Replay diagnostics reveal issues like forgetting and instability, highlighting the importance of snapshot-level analysis.
Significance
This work advances the evaluation of self-evolving LLM agents by providing a systematic, multi-view framework that captures the entire evolution process. It addresses the limitations of static benchmarks, enabling detailed analysis of update effects, transferability, and stability. The approach facilitates the development of more reliable and adaptable models, bridging the gap between research and real-world deployment, and fostering standardized evaluation protocols for continuous learning systems.
Technical Contribution
The paper introduces SEAGym, a unified environment that converts static benchmarks into dynamic, self-evolution capable task sources. It integrates with Harbor for task execution and snapshot management, supporting multi-schedule parameters. The evaluation system includes multiple metrics—validation gain, transfer success, replay stability, and cost—recorded at each snapshot, enabling detailed process diagnostics. The experimental comparison of ACE, TF-GRPO, and AHE demonstrates the framework’s ability to quantify diverse evolution dynamics and stability issues, providing a comprehensive toolkit for ongoing research.
Novelty
This is the first comprehensive framework to systematically evaluate self-evolving agents across multiple perspectives, integrating dynamic task sources, flexible scheduling, and multi-view diagnostics. Unlike prior static benchmarks, SEAGym captures the entire evolution trajectory, enabling detailed analysis of update effects, transferability, and stability. Its modular design and multi-view metrics fill a critical gap in the evaluation of continuous learning systems, setting a new standard for research in this domain.
Limitations
- The current environment mainly focuses on text-based tasks; multi-modal scenarios remain unexplored, limiting real-world applicability.
- Scheduling parameters, while flexible, require manual tuning, which can be complex and data-intensive.
- Metrics primarily assess transfer and validation, with less emphasis on inference speed, resource consumption, and deployment robustness.
Future Work
Future directions include extending SEAGym to multi-modal tasks, developing automated scheduling and hyperparameter tuning, and integrating real-world deployment constraints. Exploring meta-learning and reinforcement learning strategies for adaptive scheduling and stability improvements is also planned, aiming to make self-evolving systems more autonomous and robust in complex environments.
AI Executive Summary
The rapid development of large language models (LLMs) has transformed natural language processing, yet their ability to adapt and improve continuously remains underexplored. Traditional evaluation methods focus on static performance metrics, which fail to capture the dynamic nature of self-evolving agents that modify prompts, memories, tools, and execution strategies over time. Addressing this gap, SEAGym introduces a comprehensive, multi-view evaluation environment tailored for self-evolving LLM agents.
This framework converts static benchmarks into dynamic task sources, supporting flexible scheduling of updates, validation, and transfer tests. It records snapshots and metrics at key points, enabling detailed analysis of how different self-evolution strategies—such as ACE, TF-GRPO, and AHE—affect performance, stability, and cost. Experiments on Terminal-Bench 2.0 and HLE demonstrate that while AHE achieves the highest validation and transfer gains, it also introduces instability and forgetting issues, especially under certain batch sizes and source diversities.
The significance of this work lies in its ability to systematically dissect the evolution process, revealing that frequent updates do not guarantee persistent improvements and that source diversity and model backends significantly influence stability. By providing a standardized, diagnostic-rich environment, SEAGym paves the way for more reliable and generalizable self-evolving models, bridging the gap between research and practical deployment.
Despite its strengths, the current system mainly targets text tasks and requires manual tuning of scheduling parameters. Future work aims to extend support to multi-modal scenarios, automate hyperparameter optimization, and incorporate real-world constraints, ultimately fostering more autonomous and robust continuous learning systems. Overall, SEAGym marks a crucial step toward understanding and advancing the self-evolution of large models in complex, real-world environments.
Deep Analysis
Background
近年来，随着GPT、BERT、T5等大模型的崛起，模型规模和性能不断突破，推动自然语言处理快速发展。传统评估体系多关注静态任务集，忽视模型在实际应用中的持续学习能力。近年来，研究开始关注模型的自我演化能力，包括Prompt优化、记忆管理和工具使用等，旨在实现模型的动态适应。相关工作如ReAct、Toolformer和Self-Refine提出了模型自我改进机制，但缺乏系统化评估体系，难以全面衡量模型在连续演化中的表现。现有评估多局限于孤立任务或快照，未能充分反映模型在复杂环境中的持续适应能力。
Core Problem
核心问题在于如何科学评估大模型在持续演化中的性能变化。现有方法多关注最终效果，忽略快照质量、迁移能力、遗忘与回退等关键指标。缺乏统一的评估框架，难以比较不同策略的优劣，也限制了模型在实际中的稳定性和可靠性。设计支持多视角、多调度参数的评估体系成为亟待解决的难题。
Innovation
本研究提出SEAGym，创新点包括：1）将静态基准转化为动态任务源，支持持续训练与验证；2）引入多调度参数，支持单任务、在线和周期性演化；3）多视角评估体系，涵盖验证、迁移、重放和成本指标；4）结合Harbor平台，实现任务调度与快照管理。这一框架能系统分析不同演化策略的表现差异，提供丰富的诊断信息，推动模型持续优化。
Methodology
- �� 任务采样：从静态基准中采样训练批次、验证集、测试集。• 环境设计：定义调度参数（如批次大小、更新频率、快照时机），支持多种演化策略。• 快照管理：在关键节点保存模型状态，支持验证与迁移测试。• 评估指标：验证增益、迁移成功率、遗忘率和成本。• 交互流程：模型在每轮任务中执行、反馈、更新，环境记录快照和指标。• 多视角分析：通过不同视角检测模型的持续学习效果和稳定性。
Experiments
采用Terminal-Bench 2.0和HLE数据集，比较ACE、TF-GRPO和AHE在不同调度参数下的表现。设置五轮训练，批次大小为10、20、40、80，评估验证、ID迁移、OOD迁移和重放效果。通过快照记录和指标分析，揭示不同策略的优劣。源多样性和重放稳定性分析验证模型在复杂环境中的表现，验证模型的持续学习能力和稳定性。
Results
AHE在验证、ID和OOD迁移中表现优异，验证增益最高达17.1个百分点，但在源迁移中表现不一，部分快照出现崩溃。批次20在验证和ID迁移中效果最佳，批次80和10表现不佳。源多样性实验显示，结合HLE源能提升迁移性能。重放分析揭示模型存在遗忘和回退，快照诊断帮助识别中间崩溃点，为模型稳定性提供改进方向。
Applications
该平台适用于模型持续学习策略的系统评估，帮助研究者设计更稳定的演化机制。工业界可借助SEAGym优化模型维护流程，实现模型在实际环境中的持续适应与优化，提升智能系统的鲁棒性。
Limitations & Outlook
目前主要针对文本任务，尚未支持多模态和实际部署场景。调度参数调优复杂，缺乏自动化机制。评估指标偏重验证和迁移，未充分考虑推理速度和资源消耗。未来将扩展多模态支持，优化调度策略，提升实用性。
Plain Language Accessible to non-experts
想象你在经营一家工厂，工厂每天都在生产不同的产品。每次生产后，你会检查产品质量，调整机器参数，然后再生产下一批。SEAGym就像这个工厂的管理系统，它能记录每次调整的效果，帮助你找到最优的生产策略。不同的调节方法（比如调整机器速度或换用不同的原料）会影响最终产品的质量。这个系统还能告诉你，哪些调整是有效的，哪些可能导致问题。通过不断试错和总结经验，工厂的效率会逐步提高。这个过程就像模型在学习中不断改进自己的策略和记忆，确保每次“生产”都比上次更好。它让我们用科学的方法管理和优化复杂的系统，避免盲目试错，逐步实现智能化升级。
ELI14 Explained like you're 14
想象你在玩一款超级复杂的游戏，你的角色需要不断学习新技能，调整策略才能打败更强的敌人。每次打完一场战斗，你会总结经验，改进装备或技能，然后再去挑战。SEAGym就像是这个游戏的教练，它会记录你的每次战斗表现，帮你分析哪些策略有效，哪些需要改进。它还能模拟不同的战斗场景，测试你的技能是否能应对各种变化。通过不断试错和调整，你的角色会变得越来越厉害。这种方法让你在游戏中不断学习、变强，而不是每次都从零开始。它用科学的方法帮助模型像你一样变得更聪明、更强大，逐步掌握更多技能，赢得更多胜利。
Abstract
Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop. Existing evaluations often reduce this process to isolated task scores or a single sequential curve, obscuring whether an update produces reusable improvement, overfits recent tasks, increases cost, or harms older behavior. We introduce SEAGym, an evaluation environment for measuring agent harness updates across training, validation, test, replay, and cost records. SEAGym turns Harbor-compatible benchmarks into dynamic self-evolution task sources with train batches, frozen update-validation, held-out ID and OOD transfer views, replay diagnostics, and saved snapshot and metric records. Instantiating SEAGym on Terminal-Bench 2.0 and HLE, we compare ACE, TF-GRPO, and AHE under a shared epoch/batch protocol. The results show that these evaluation views provide complementary signals about the evolution process: frequent updates may fail to improve held-out performance, useful intermediate snapshots may collapse later, and source diversity and model backend can affect harness reliability.