Posts
All the articles I've posted.
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Large Language Model Compression with Global Rank and Sparsity Optimization
This paper introduces a two-stage LLM compression method using RPCA for low-rank and sparse decomposition and probabilistic pruning via policy gradient, outperforming state-of-the-art techniques at a 50% compression ratio while automatically adapting to layer-wise redundancy without manual thresholds or extensive fine-tuning.
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Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
This paper introduces Latent Preference Coding (LPC), a framework that uses discrete latent codes to model multifaceted human preferences, consistently improving the performance of offline alignment algorithms like DPO, SimPO, and IPO across multiple LLMs and benchmarks.
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Rethinking Meta-Learning from a Learning Lens
This paper rethinks meta-learning from a 'learning' lens, proposing TRLearner, a plug-and-play method that leverages task relations to calibrate optimization, demonstrating significant performance improvements across regression, classification, drug activity, pose prediction, and OOD generalization tasks.
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Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
本文首次系统调查了大型语言模型高效推理的进展,通过分类模型、输出和提示-based方法,探讨了减少"过度思考"现象的策略,以优化计算效率并保持推理能力。
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Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework
本文提出了LMGT框架,通过利用大型语言模型的先验知识对强化学习的奖励进行动态调整,有效平衡了探索与利用,显著提高了样本效率并降低了训练成本,并在多种环境、算法以及机器人和推荐系统等复杂场景中验证了其有效性。