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• The proposed method uses LoRA adapters combined with supervised fine-tuning to enable reasoning in small LLMs.method 95%
• Budget forcing via reinforcement learning on adapters significantly reduces response length with minimal accuracy loss.method 90%
• Parallel test-time scaling improves accuracy at minor latency increase.technical 90%
• A dynamic adapter-switching mechanism activates reasoning only when needed.technical 95%
• KV-cache sharing during prompt encoding reduces time-to-first-token for on-device inference.technical 90%
• Experiments on Qwen2.5-7B demonstrate efficient, accurate reasoning under strict resource constraints.result 95%
• The method makes LLM reasoning practical for mobile scenarios.market 85%
• Existing distillation approaches produce verbose and stylistically redundant reasoning traces undesirable for on-device inference.limitation 90%

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