Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning

The paper investigates how to fine‑tune language models for sparse and continual reinforcement learning by ranking and partitioning attention heads. For sparse learning, heads are ranked by gradient norm to identify a small, effective subset for training. For continual learning, heads are partitioned across tasks to reduce interference and preserve prior knowledge. Empirical results on math, instruction‑following, and coding tasks show that tuning higher‑ranked heads outperforms lower‑ranked ones and matches denser baselines, while head partitioning improves retention without sacrificing adaptation to new tasks.

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• llms
• reinforcement-learning
• fine-tuning