Large Language Models

RealCritic: A new benchmark effectively evaluates language models’ critique abilities using a closed-loop methodology, showcasing advanced reasoning models’ superiority in self and iterative critique.

Humanity’s Last Exam (HLE): a groundbreaking multi-modal benchmark pushing the boundaries of large language model (LLM) capabilities, revealing a significant gap between current LLMs and human experts…

SIGMA, a novel large language model, achieves up to 33.36% faster inference speeds by using DiffQKV attention, which differentially optimizes query, key, and value components in the attention mech…

Low-rank adapters combined with neural architecture search revolutionize LLM compression, enabling efficient fine-tuning and significantly reduced memory footprint.

Large language models (LLMs) are rapidly evolving, yet often struggle to adapt to human preferences quickly. This paper introduces Test-Time Preference Optimization (TPO), an innovative framework that…

Pairwise RM, a novel reward model with knockout tournaments, significantly boosts large language model accuracy in test-time scaling by comparing solution pairs, eliminating arbitrary scoring inconsis…

O1-Pruner efficiently prunes long-thought reasoning in LLMs by harmonizing reasoning length and accuracy via fine-tuning, significantly reducing inference time without sacrificing performance.

Kimi K1.5: A Multimodal LLM trained with RL achieves state-of-the-art reasoning by scaling long context RL training and improving policy optimization.

DeepSeek-R1 significantly improves LLM reasoning by using reinforcement learning, achieving performance comparable to OpenAI’s top models while addressing previous challenges of poor readability and l…

Revolutionizing large language models, Autonomy-of-Experts (AoE) empowers individual expert modules to autonomously select inputs, eliminating routers and boosting both efficiency and accuracy.

Debate-enhanced weak supervision boosts AI alignment by combining strong and weak models, enabling safer and more reliable AI systems.

This research proposes principles and a framework to tackle redundancy in MLLM benchmarks, enhancing efficiency and guiding future development.

Democratizing advanced reasoning in AI, this blueprint introduces a modular framework for building Reasoning Language Models (RLMs), simplifying development and enhancing accessibility.

Mobile-Agent-E: A self-evolving mobile assistant conquering complex tasks with hierarchical agents and a novel self-evolution module, significantly outperforming prior approaches.

Agent-R: A novel self-training framework enables language model agents to learn from errors by dynamically constructing training data that corrects erroneous actions, resulting in significantly improv…

STEP-KTO: A novel training framework boosts LLMs’ mathematical reasoning by providing binary feedback on both intermediate steps and final answers. This ensures logical reasoning trajectories and impr…

PaSa: An LLM agent autonomously performs comprehensive academic paper searches, outperforming existing methods by efficiently combining search tools, paper reading, and citation analysis, optimized vi…

Mind Evolution, a novel evolutionary search strategy, significantly boosts Large Language Model (LLM) problem-solving by generating, recombining, and refining candidate solutions via an LLM, outperfor…

ComplexFuncBench, a new benchmark, rigorously evaluates LLMs’ complex function-calling abilities across real-world scenarios involving multi-step processes, constraints, and long contexts.

This survey paper explores the exciting new frontier of Large Reasoning Models (LRMs), focusing on how reinforcement learning and clever prompting techniques are boosting LLMs’ reasoning capabilities.