Training large language models faces frequent interruptions due to various faults, demanding robust fault-tolerance. Existing backup-free methods, such as redundant computation, dynamic parallelism, and data rerouting, each incur performance penalties, whether from ongoing overhead, lengthy reconfigurations, or post-recovery inefficiencies. We propose Chameleon, an adaptive fault-tolerant system that intelligently selects optimal recovery strategies when a failure occurs. Chameleon achieves this through a unified performance model, expedient execution plan search, accurate performance estimation, and efficient communication optimizations. Experiments on a 32-card cluster show that Chameleon maintains a performance gap of within 11.00% between post-recovery and failure-free training, while preserving model convergence and efficient memory usage. Compared to state-of-the-art methods, Chameleon achieves up to 1.229x and 1.355x higher average throughput than Oobleck and Recycle, respectively.

To model the context history in multi-turn conversations has become a critical step towards a better understanding of the user query in question answering systems. To utilize the context history, most existing studies treat the whole context as input, which will inevitably face the following two challenges. First, modeling a long history can be costly as it requires more computation resources. Second, the long context history consists of a lot of irrelevant information that makes it difficult to model appropriate information relevant to the user query. To alleviate these problems, we propose a reinforcement learning based method to capture and backtrack the related conversation history to boost model performance in this paper. Our method seeks to automatically backtrack the history information with the implicit feedback from the model performance. We further consider both immediate and delayed rewards to guide the reinforced backtracking policy. Extensive experiments on a large conversational question answering dataset show that the proposed method can help to alleviate the problems arising from longer context history. Meanwhile, experiments show that the method yields better performance than other strong baselines, and the actions made by the method are insightful.

Zehao Lin, Xinjing Huang, Feng Ji, Haiqing Chen, Yin Zhang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.