AREAL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models
arXiv:2602.00482v2 Announce Type: replace Abstract: Reinforcement learning (RL)-based post-training for large language models (LLMs) is computationally expensive, as it generates many rollout sequences that frequently share long token prefixes. Existing RL frameworks usually process these sequences independently during policy training, i.e., repeatedly recomputing identical prefixes in both the forward and backward passes of policy gradient computation, leading to substantial inefficiencies in computation resources and memory usage. Although prefix sharing naturally induces a tree structure ov
The increasing scale and computational cost of training large language models with reinforcement learning necessitate more efficient algorithms to make the process sustainable.
This research directly addresses the high computational and memory demands of current RL-based LLM training, which is a major bottleneck for advanced AI development.
Optimized algorithms like AREAL-DTA reduce the resources needed for LLM reinforcement learning, potentially accelerating the development and deployment of more sophisticated AI models.
- · AI model developers
- · Cloud computing providers (through more efficient usage)
- · LLM-powered application developers
- · Inefficient RL training methods
- · Companies with less sophisticated AI infrastructure
More efficient LLM training reduces operational costs for AI research and development.
Faster and cheaper training cycles could lead to more rapid iteration and deployment of advanced AI functionalities.
The democratization of advanced LLM capabilities might accelerate the adoption of AI agents across various industries.
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Read at arXiv cs.LG