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ARXIV:2603.16867 · EFFICIENT LLM DEPLOYMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16867EFFICIENT LLM DEPLOYMENTSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEYelysei Bondarenko · Thomas Hehn · Rob Hesselink · Romain Lepert · Fabio Valerio Massoli · Evgeny Mironov · +12 at arXiv
A lightweight approach to enable efficient reasoning in small LLMs for mobile devices using LoRA adapters and reinforcement learning.
Opportunity summary
Pain A lightweight approach to enable efficient reasoning in small LLMs for mobile devices using LoRA adapters and reinforcement learning.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A lightweight approach to enable efficient reasoning in small LLMs for mobile devices using LoRA adapters and reinforcement learning. These challenges include high token generation costs, large KV-cache footprints, and inefficiencies when distilling reasoning…
Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment. These challenges include high token…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make…
Efficient LLM Deployment moved forward this cycle; last verified April 2026. Public score 8.0/10.
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A lightweight approach to enable efficient reasoning in small LLMs for mobile devices using LoRA adapters and reinforcement learning.
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10.48550/arXiv.2603.16867A lightweight approach to enable efficient reasoning in small LLMs for mobile devices using LoRA adapters and reinforcement learning.
Abstract
Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment. These challenges include high token generation costs, large KV-cache footprints, and inefficiencies when distilling reasoning capabilities into smaller models for mobile devices. Existing approaches often rely on distilling reasoning traces from larger models into smaller models, which are verbose and stylistically redundant, undesirable for on-device inference. In this work, we propose a lightweight approach to enable reasoning in small LLMs using LoRA adapters combined with supervised fine-tuning. We further introduce budget forcing via reinforcement learning on these adapters, significantly reducing response length with minimal accuracy loss. To address memory-bound decoding, we exploit parallel test-time scaling, improving accuracy at minor latency increase. Finally, we present a dynamic adapter-switching mechanism that activates reasoning only when needed and a KV-cache sharing strategy during prompt encoding, reducing time-to-first-token for on-device inference. Experiments on Qwen2.5-7B demonstrate that our method achieves efficient, accurate reasoning under strict resource constraints, making LLM reasoning practical for mobile scenarios. Videos demonstrating our solution running on mobile devices are available on our project page.
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Dimensions overall score 8.0
PROBLEM
A lightweight approach to enable efficient reasoning in small LLMs for mobile devices using LoRA adapters and reinforcement learning. These challenges include high token generation costs, large KV-cache footprints, and inefficiencies when distilling reasoning capabilities into s...
METHOD
Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment. These challenges include high toke...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements ma...
WHY NOW
Efficient LLM Deployment moved forward this cycle; last verified April 2026. Public score 8.0/10.
In this work, we propose a lightweight approach to enable reasoning in small LLMs using LoRA adapters combined with supervised fine-tuning.
Explicitly stated in the abstract as a core component of the proposed approach
partial
We further introduce budget forcing via reinforcement learning on these adapters, significantly reducing response length with minimal accuracy loss.
Directly stated in the abstract with clear performance claim
partial
To address memory-bound decoding, we exploit parallel test-time scaling, improving accuracy at minor latency increase.
Explicitly stated in the abstract as a technical improvement
partial
Finally, we present a dynamic adapter-switching mechanism that activates reasoning only when needed
Directly stated in the abstract as a specific technical innovation
partial
and a KV-cache sharing strategy during prompt encoding, reducing time-to-first-token for on-device inference.
Explicitly stated in the abstract with clear performance benefit
partial
Experiments on Qwen2.5-7B demonstrate that our method achieves efficient, accurate reasoning under strict resource constraints
Directly stated in the abstract with specific model mentioned
partial
making LLM reasoning practical for mobile scenarios.
Directly stated in the abstract as the outcome of the research
partial
Existing approaches often rely on distilling reasoning traces from larger models into smaller models, which are verbose and stylistically redundant, undesirable for on-device inference.
Directly stated in the abstract as a limitation of existing approaches
partial
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A lightweight approach to enable efficient reasoning in small LLMs for mobile devices using LoRA adapters and reinforcement learning.
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Efficient LLM Deployment
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