TL;DR
Lookahead Optimization for Rehearsal (LOR) is introduced, establishing a new and more robust paradigm for rehearsal-based OCIL and significantly out-performs state-of-the-art methods.
Continual Learning (CL) for Large Language Models (LLMs) faces a fundamental Stability-Plasticity Dilemma : balancing the plasticity to acquire new capabilities with the stability to preserve prior knowledge. While Parameter-Efficient Fine-Tuning methods, such as LoRA, enable efficient adaptation, we identify a critica...
To mitigate a critical imbalance during the exploration-and-learning process, this work approaches head-tail re-balance during the exploration-and-learning process from two perspectives: distribution-reshaping and trajectory-resampling.
A systematic evaluation of representative task-adaptation methods shows that task adaptation is not merely a capability-improving step, but an alignment intervention in its own right, motivating multi-dimensional alignment evaluation as a standard component of post-training pipelines.
This work proposes Socialized Division and Collaboration as a reformulation of continual learning that decomposes session learning across specialized models in response to optimization conflicts, while enabling coordinated collaboration and introduces an energy-based session-model compatibility criterion grounded in He...
A novel contextual bandit algorithm that explicitly incorporates reward decay modeling that achieves significant performance gains over strong baselines and confirms that the integration of reward decay modeling within the bandit framework is crucial for mitigating over-exploitation and optimizing the iterative refinem...
This work formalizes CL as an online optimization problem governed by the interaction between environmental and learning dynamics, and introduces Transfer Efficiency as a quantitative measure of the tension between Instability, the bias inherited from conflicting past experience, and Transient Error, the optimization c...
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