LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning

TL;DR

LongAct introduces a saliency-guided sparse update method that leverages intrinsic activation patterns in LLMs to improve long-context reasoning in reinforcement learning, achieving notable performance gains.

Contribution

The paper presents LongAct, a novel approach that selectively updates significant activation weights, enhancing long-context RL performance across various algorithms.

Findings

Achieves approximately 8% improvement on LongBench v2.

Enhances generalization on the RULER benchmark.

Boosts performance across multiple RL algorithms like GRPO and DAPO.

Abstract

Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent advancements have focused on reward engineering or data synthesis, few studies exploit the model's intrinsic representation characteristics to guide the training process. In this paper, we first observe the presence of high-magnitude activations within the query and key vectors when processing long contexts. Drawing inspiration from model quantization -- which establishes the criticality of such high-magnitude activations -- and the insight that long-context reasoning inherently exhibits a sparse structure, we hypothesize that these weights serve as the pivotal drivers for effective model optimization. Based on this insight, we propose LongAct, a strategy that shifts from uniform to saliency-guided sparse updates. By selectively updating only the weights associated with these significant activations, LongAct achieves an approximate 8% improvement on LongBench v2 and enhances generalization on the RULER benchmark. Furthermore, our method exhibits remarkable universality, consistently boosting performance across diverse RL algorithms such as GRPO and DAPO. Extensive ablation studies suggest that focusing on these salient features is key to unlocking long-context potential.