Introduces the first benchmark for fine-grained failures in reinforcement fine-tuning of LLMs and an automatic management framework that detects, diagnoses, and remediates them.
Aiops solutions for incident management: Technical guidelines and a com- prehensive literature review
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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background 1
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cs.SE 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
E2E-REME outperforms nine LLMs in accuracy and efficiency for end-to-end microservice remediation by using experience-simulation reinforcement fine-tuning on a new benchmark called MicroRemed.
LightGBM with team-level features outperforms a bank's existing rule-based change risk process on a one-year dataset while using SHAP for regulatory explainability.
citing papers explorer
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Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
Introduces the first benchmark for fine-grained failures in reinforcement fine-tuning of LLMs and an automatic management framework that detects, diagnoses, and remediates them.
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E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning
E2E-REME outperforms nine LLMs in accuracy and efficiency for end-to-end microservice remediation by using experience-simulation reinforcement fine-tuning on a new benchmark called MicroRemed.
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Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
LightGBM with team-level features outperforms a bank's existing rule-based change risk process on a one-year dataset while using SHAP for regulatory explainability.