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Found 12 papers, 3 papers with code
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking
However, these methods encounter challenges with complex planning tasks, primarily due to extended reasoning steps, diverse constraints, and the challenge of handling multiple distinct sub-tasks.
Accelerating Large Language Model Reasoning via Speculative Search
Experiments on both the Qwen and Llama models demonstrate that SpecSearch significantly outperforms state-of-the-art approaches, achieving up to 2. 12$\times$ speedup with comparable reasoning quality.
AttentionPredictor: Temporal Pattern Matters for Efficient LLM Inference
With the development of large language models (LLMs), efficient inference through Key-Value (KV) cache compression has attracted considerable attention, especially for long-context generation.
Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms
We executed six state-of-the-art AI-based chip placement algorithms on these designs and plugged the results of each single-point algorithm into the physical implementation workflow to obtain the final PPA results.
Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well.
Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming
A key problem for cuts is when to stop cuts generation, which is important for the efficiency of solving MILPs.
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning
Specifically, we formulate the routine design task as a Markov decision process and propose an RL framework with adaptive action sequences to generate high-quality presolve routines efficiently.
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
In particular, we notice that the runtime of the Resub and Mfs2 operators often dominates the overall runtime of LS optimization processes.
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Cut selection -- which aims to select a proper subset of the candidate cuts to improve the efficiency of solving MILPs -- heavily depends on (P1) which cuts should be preferred, and (P2) how many cuts should be selected.
Efficient Exploration in Resource-Restricted Reinforcement Learning
In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode.
