The paper addresses the challenge of serving long-context LLMs on resource-constrained mobile edge networks. It proposes a test-time deep reinforcement learning framework to dynamically manage model caching and inference offloading, optimizing system costs and maintaining performance.
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📌 Adaptive Offloading vs. Static Heuristics
Traditional caching and offloading methods like FIFO and LFU (Least Frequently Used) are static and fail in dynamic LLM scenarios. Test-time deep reinforcement learning (T2DRL) adapts in real-time, optimizing decisions based on current conditions. This eliminates inefficiencies from predefined rules, reducing system costs by 30%.
📌 Market-Driven Edge Resource Allocation
The double Dutch auction mechanism ensures optimal resource distribution by dynamically balancing supply and demand. Unlike fixed allocation strategies, this approach maximizes social welfare and prevents underutilization or congestion.
📌 Inference Cost vs. Context Length Trade-off
LLMs require long context windows, but edge devices have limited memory and compute. T2DRL efficiently balances inference accuracy and cost, outperforming PPO by 20%. It learns optimal caching and offloading strategies without prior assumptions, adapting as requests evolve.
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https://arxiv.org/abs/2501.14205
Original Problem 🤔:
→ Mobile edge networks struggle to support long-context LLM serving due to limited resources.
→ Existing offloading frameworks are not suitable for LLMs because they do not account for dynamic learning and context evolution.
→ Balancing context length, accuracy, and performance in LLM serving at the edge is challenging.
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Solution in this Paper 💡:
→ This paper proposes a joint model caching and inference offloading framework using test-time deep reinforcement learning.
→ The framework minimizes system cost under hardware and context window constraints.
→ A test-time deep reinforcement learning algorithm is introduced with a test-time training model-based actor-critic network.
→ This algorithm learns caching and offloading strategies by interacting with the environment in real-time during both training and testing phases.
→ A double Dutch auction mechanism further enhances resource allocation efficiency and maximizes social welfare by dynamically matching resource supply and user demand.
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Key Insights from this Paper 🔑:
→ LLM serving at the edge differs significantly from traditional computation and service offloading due to LLMs' context-aware nature.
→ Model caching at edge servers is crucial for efficient LLM agent provisioning.
→ Test-time deep reinforcement learning can proactively adapt model caching and inference strategies to dynamic requests and contexts.
→ Double Dutch auction mechanism can efficiently allocate resources in a dynamic LLM serving market.
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Results 📊:
→ T2DRL reduces system costs by at least 30% compared to FIFO, LFU, and Cloud baselines.
→ T2DRL reduces system cost by 20% compared to PPO algorithm.
→ T2DRL achieves higher reasoning accuracy across datasets like MultiArith and ARC compared to baselines.
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