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Matching papers

Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training

149 upvotes · 27 AUG 2026 · Tingyun Li, Wenfeng Feng, Weiqing Li et al.

This paper proposes a method to determine which past update evidence in a large language model is still relevant and useful after subsequent training, to prevent wasting compute and potentially degrading the model's performance. Practitioners in the field of autonomous systems and language models might care about this problem because it can lead to better model performance and efficiency in adapting to changing domains and requirements.

continual learningexperience transferlarge language modelspost-trainingautonomous systems

Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models

74 upvotes · 8 SEP 2026 · Zongjie Li, Alan Z. W, John Nicolas J et al.

This paper presents a data-centric framework to overcome challenges in training cyber agents, allowing for more efficient and effective training of open-weight models, and demonstrates the capability of a team of seven to train such models with leading agentic cyber capability.

continual learningcyber modelsopen-weight modelspost-trainingresettable environments