FGGM: Fisher-Guided Gradient Masking for Continual Learning
Catastrophic forgetting impairs the continuous learning of large language models. We propose Fisher-Guided Gradient Masking (FGGM), a framework that mitigates this by strategically selecting parameters for updates using diagonal Fisher Information. FGGM dynamically generates binary masks with adaptive thresholds, preserving critical parameters to balance stability and plasticity without requiring historical data. Unlike magnitude-based methods such as MIGU, our approach offers a mathematically principled parameter importance estimation. On the TRACE benchmark, FGGM shows a 9.6% relative improvement in retaining general capabilities over supervised fine-tuning (SFT) and a 4.4% improvement over MIGU on TRACE tasks. Additional analysis on code generation tasks confirms FGGM's superior performance and reduced forgetting, establishing it as an effective solution.
Code (0)
등록된 구현이 없습니다.
Tasks
Continual LearningCode GenerationSimilar Papers 제목 키워드 기반
Fisher-Orthogonal Projected Natural Gradient Descent for Continual Learning
Continual learning aims to enable neural networks to acquire new knowledge on sequential tasks. However, the key challenge in such settings is to learn new tasks without catastrophically forgetting previously learned tas…
Continual LearningSEVerA: Verified Synthesis of Self-Evolving Agents
Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm, a planner LLM synthesizes an agent program that invokes parametric mode…
Code GenerationProgram RepairRegularizing Trajectories to Mitigate Catastrophic Forgetting
Regularization-based continual learning approaches generally prevent catastrophic forgetting by augmenting the training loss with an auxiliary objective. However in most practical optimization scenarios with noisy data a…
Continual LearningTrust Region Continual Learning as an Implicit Meta-Learner
Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standard strategies face a core tradeoff: regularization-based methods (e.g., EWC) can overconstrain updates when task optima are…
Continual LearningImage GenerationRevisiting Softmax Masking: Stop Gradient for Enhancing Stability in Replay-based Continual Learning
In replay-based methods for continual learning, replaying input samples in episodic memory has shown its effectiveness in alleviating catastrophic forgetting. However, the potential key factor of cross-entropy loss with …
Continual LearningIncremental Learning