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GRIP: Granular Reward-Guided Parameter Interpolation for Efficient Reasoning
Reasoning-oriented large language models often achieve strong problem-solving performance by generating long chains of thought, but this behavior substantially increases inference cost and latency. In contrast, instruction-tuned models tend to answer more concisely, yet often lack comparable reasoning ability. This accuracy-efficiency mismatch motivates a lightweight approach that combines the strengths of both models without full model retraining. In this paper, we propose GRIP (Granular Reward-guided Interpolation of Parameters), a reward-guided parameter interpolation framework for efficien
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T09:51:41.000Z
First collected: 2026-09-21T09:22:01.459Z. This is not the publication date.