Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search The paper presents Mulberry, an MLLM with step-by-step reasoning and reflection, powered by CoMCTS. CoMCTS uses collective knowledge from multiple models to efficiently
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Align-Anything: Training Multimodal Models with Language Feedback
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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback The paper introduces the Align-Anything framework to enhance all-modality models' alignment with human preferences using language feedback, improving instruction-following across text,
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Reproducing o1: Reinforcement Learning Search Scaling Roadmap
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Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective This paper presents a roadmap for reproducing OpenAI's o1, an advanced LLM that excels in reasoning. It focuses on four key components—policy initialization, reward design, search,
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Advanced Reasoning Models and Multimodal AI Reshape Reality
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From more and more advanced reasoning models to reconstructing the world in 3D—AI is reshaping reality. What a way to cap the year! – Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
– Align Anything: Training All-Modality -
Open-Source Advantage in Large Language Models Debate
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The Open-Source Advantage in Large Language Models (LLMs) Large Language Models (LLMs) are revolutionizing natural language processing, but the debate between closed-source and open-source models raises important questions about transparency, accessibility, and ethics.
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Grokking Complexity Dynamics: Memorization to Generalization
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The Complexity Dynamics of Grokking A study on how neural network complexity dynamics explain the grokking phenomenon, where models transition from memorization to generalization long after overfitting. Problem: Grokking challenges our understanding of generalization in
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LLM Agents Evolve Cooperation Through Indirect Reciprocity
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Cultural Evolution of Cooperation among LLM Agents A study examining how large language model (LLM) agents evolve cooperation and social norms over generations, specifically focusing on indirect reciprocity. Problem: Limited understanding of how multiple LLM agents interact
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Adaptive Computation Modules: Efficient Token-Level Conditional Inference
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Adaptive Computation Modules: Granular Conditional Computation for Efficient Inference A neural network module that dynamically adapts computational load per token, reducing inference costs without sacrificing accuracy. Problem: Transformer models are computationally
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AniDoc: AI-Powered 2D Animation Colorization and In-Betweening
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AniDoc: Animation Creation Made Easier A tool leveraging generative AI to automate 2D animation colorization and in-betweening for more efficient animation production. Problem: The 2D animation process, especially line art colorization and in-betweening, is
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LlamaFusion Extends LLMs with Multimodal Capabilities
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LlamaFusion Extends pretrained text-only LLMs with multimodal capabilities while preserving language performance. Problem:
Training multimodal models from scratch is costly and risks degrading pretrained language abilities. Method:
Adds image-specific modules to pretrained