arXiv:2606.03113v1 Announce Type: new Abstract: Large Language Models suffer from slow autoregressive inference. While self-speculative decoding accelerates this process, its efficiency is hampered by static configurations like fixed exit layers and speculation lengths. We reframe this optimization as a \textbf{Markov Decision Process} and propose \textbf{LEDE}, a framework that uses offline reinforcement learning. LEDE learns a policy to dynamically select the optimal exit layer and speculation length based on the local context of the generated sequence at each step, balancing computational c
The increasing scale and deployment of Large Language Models (LLMs) are driving urgent efforts to optimize inference speed and efficiency, making dynamic exits a critical area of research.
Improving LLM inference efficiency directly translates to lower operational costs, faster response times, and broader applicability of AI, which is crucial for competitive advantage in AI product development and deployment.
This research introduces a method for LLMs to dynamically optimize their inference process in real-time, potentially reducing computational overhead and accelerating performance without fixed configurations.
- · AI model developers
- · Cloud providers
- · AI-powered applications
- · LLM users
- · Inefficient LLM inference methods
LLMs can perform inference more quickly and with fewer computational resources.
The cost-effectiveness of deploying larger, more capable LLMs in real-time applications will improve, accelerating their adoption.
Increased LLM efficiency could lead to a proliferation of more complex and interactive AI agents, as the barrier to real-time interaction decreases.
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Read at arXiv cs.CL