Large Language Models

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…

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.

LLM reasoning boosts self-confidence, even when answers are wrong, highlighting limitations in current evaluation metrics.

Arabic LLMs struggle with medical tasks; this study reveals optimal language ratios in training data for improved performance, highlighting challenges in simply translating medical data for different …

RLHS, a novel alignment algorithm, leverages simulated hindsight feedback to mitigate misalignment in RLHF, significantly improving AI’s alignment with human values and goals.

URSA-7B: A new multimodal model significantly improves chain-of-thought reasoning in mathematics!

Small language models can master complex math reasoning using self-evolved deep thinking via Monte Carlo Tree Search, surpassing larger models in performance.

LLMs revolutionize scientific research! This survey reveals their transformative potential across hypothesis discovery, experiment planning, writing, and peer review, guiding future research.

EpiCoder revolutionizes code generation by using feature trees to create diverse and complex training data, resulting in state-of-the-art performance on various benchmarks.

Boosting private LLMs’ efficiency and security, this research introduces an entropy-guided attention mechanism and PI-friendly layer normalization to mitigate the overheads of nonlinear operations.

BoostStep enhances large language models’ mathematical abilities by refining single-step reasoning through a novel step-level in-context learning strategy, achieving significant improvements on variou…

ToolHop: New benchmark dataset rigorously evaluates LLMs’ multi-hop tool use, revealing significant challenges and variations across different LLM families.

Unlocking LLM potential: This paper surveys test-time computing, showing how it boosts reasoning abilities by shifting from reactive System-1 to deliberate System-2 thinking, paving the way for more p…

New scaling laws for efficient floating-point quantization training in LLMs are presented, showing optimal bit allocation and critical data size.

REINFORCE++, a novel RLHF algorithm, achieves superior training stability and computational efficiency compared to existing methods like PPO and GRPO, while maintaining comparable performance.