E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning

• URL: http://arxiv.org/abs/2604.11094v1
• Date: Mon, 13 Apr 2026 07:12:04 GMT
• Title: E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning
• Abstract summary: We introduce textitEnd-to-End Microservice Remediation (E2E-MR)<n>E2E-MR generates executable playbooks from diagnosis reports to autonomously restore faulty systems.<n>We propose textitE2E-REME, an end-to-end auto-remediation model trained via experience-simulation reinforcement fine-tuning.
• Score: 36.966897385124646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary microservice systems continue to grow in scale and complexity, leading to increasingly frequent and costly failures. While recent LLM-based auto-remediation approaches have emerged, they primarily translate textual instructions into executable Ansible playbooks and rely on expert-crafted prompts, lacking runtime knowledge guidance and depending on large-scale general-purpose LLMs, which limits their accuracy and efficiency. We introduce \textit{End-to-End Microservice Remediation} (E2E-MR), a new task that requires directly generating executable playbooks from diagnosis reports to autonomously restore faulty systems. To enable rigorous evaluation, we build \textit{MicroRemed}, a benchmark that automates microservice deployment, failure injection, playbook execution, and post-repair verification. We further propose \textit{E2E-REME}, an end-to-end auto-remediation model trained via experience-simulation reinforcement fine-tuning. Experiments on public and industrial microservice platforms, compared with nine representative LLMs, show that E2E-REME achieves superior accuracy and efficiency.

Related papers

• MicroRemed: Benchmarking LLMs in Microservices Remediation [38.338663893180446]
  Large Language Models (LLMs) integrated with agent-based reasoning frameworks have recently shown strong potential for autonomous decision-making.<n>One promising yet underexplored direction is microservice remediation, where the goal is to automatically recover faulty microservice systems.<n>Existing approaches still rely on human-crafted prompts from Site Reliability Engineers (SREs)<n>We introduce MicroRemed, the first benchmark for evaluating LLMs in end-to-end microservice remediation.
  arXiv  Detail & Related papers  (2025-11-03T02:35:55Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• InternBootcamp Technical Report: Boosting LLM Reasoning with Verifiable Task Scaling [71.37579508777843]
  Large language models (LLMs) have revolutionized artificial intelligence by enabling complex reasoning capabilities.<n>To address this gap, we present InternBootcamp, an open-source framework comprising 1000+ domain-diverse task environments.
  arXiv  Detail & Related papers  (2025-08-12T05:00:00Z)
• AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents [15.41283323575065]
  AutoEDA is a framework for EDA automation that leverages paralleled learning through the Model Context Protocol (MCP) specific for standardized and scalable natural language experience.<n>Results from experiments show improvements in automation accuracy and efficiency, as well as script quality when compared to existing methods.
  arXiv  Detail & Related papers  (2025-08-01T18:23:57Z)
• MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision [76.42361936804313]
  We introduce MAS-ZERO, the first self-evolved, inference-time framework for automatic MAS design.<n> MAS-ZERO employs meta-level design to iteratively generate, evaluate, and refine MAS configurations tailored to each problem instance.
  arXiv  Detail & Related papers  (2025-05-21T00:56:09Z)
• MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering [57.156093929365255]
  Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents.<n>MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios.<n>Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-12T17:35:43Z)
• AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
  We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
  arXiv  Detail & Related papers  (2024-11-02T13:24:30Z)
• RePair: Automated Program Repair with Process-based Feedback [28.017321930042694]
  We show how small-scale language models (LM) can achieve excellent performance through process supervision and feedback. We develop a reward model that serves as a critic, providing feedback for the fine-tuned LM's action. The results show that process-based not only outperforms larger outcome-based generation methods, but also nearly matches the performance of closed-source commercial large-scale LMs.
  arXiv  Detail & Related papers  (2024-08-21T02:53:23Z)
• MicroRes: Versatile Resilience Profiling in Microservices via Degradation Dissemination Indexing [29.456286275972474]
  Microservice resilience, the ability to recover from failures and continue providing reliable and responsive services, is crucial for cloud vendors. The current practice relies on manually configured specific rules to a certain microservice system, resulting in labor-intensity and flexibility issues. Our insight is that resilient deployment can effectively prevent the dissemination of degradation from system performance to user-aware metrics, and the latter affects service quality.
  arXiv  Detail & Related papers  (2022-12-25T03:56:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.