Process-supervised reward models (PRMs) offer fine-grained, step-wise feedback on model responses, aiding in selecting effective reasoning paths for complex tasks. Unlike output reward models (ORMs), which evaluate responses based on final outputs, PRMs provide detailed assessments at each step, making them particularly valuable for reasoning-intensive applications. While PRMs have been extensively studied in language tasks, their application in multimodal settings remains largely unexplored. Most vision-language reward models still rely on the ORM approach, highlighting the need for further research into how PRMs can enhance multimodal learning and reasoning. Existing reward benchmarks primarily focus on text-based models, with some specifically designed

Text-to-SQL translation, the task of transforming natural language queries into structured SQL statements, is essential for facilitating user-friendly database interactions. However, the task involves significant complexities, notably schema linking, handling compositional SQL syntax, and resolving ambiguities in user queries. While Large Language Models (LLMs) have shown robust capabilities across various domains, the efficacy of structured reasoning techniques such as Chain-of-Thought (CoT) within text-to-SQL contexts remains limited. Prior attempts employing zero-shot CoT or Direct Preference Optimization (DPO) without structured reasoning yielded marginal improvements, indicating the necessity for more rigorous methodologies. Snowflake introduces ExCoT, a structured framework designed to optimize open-source LLMs

The decentralized platform Vana, which started as an MIT class project, is on a mission to give power back to users. The firm created a user-owned network that allows individuals to upload their data and govern how they are used to train AI models.

The rapid progress in artificial intelligence (AI) and machine learning (ML) research underscores the importance of accurately evaluating AI agents' capabilities in replicating complex, empirical research tasks traditionally performed by human researchers. Currently, systematic evaluation tools that precisely measure the ability of AI agents to autonomously reproduce ML research findings remain limited, posing challenges in fully understanding the potential and limitations of such systems. OpenAI has introduced PaperBench, a benchmark designed to evaluate the competence of AI agents in autonomously replicating state-of-the-art machine learning research. PaperBench specifically measures whether AI systems can accurately interpret research papers, independently develop the necessary

LLMs have significantly advanced NLP, demonstrating strong text generation, comprehension, and reasoning capabilities. These models have been successfully applied across various domains, including education, intelligent decision-making, and gaming. LLMs serve as interactive tutors in education, aiding personalized learning and improving students’ reading and writing skills. In decision-making, they analyze large datasets to generate insights for complex problems. LLMs enhance player experiences by generating dynamic content and facilitating strategy development within gaming. However, despite these successes, their application to intricate tasks such as strategic gameplay in Gomoku remains challenging. Gomoku, a classic board game known for its simple rules yet deep

The advancement of large language models (LLMs) has significantly influenced interactive technologies, presenting both benefits and challenges. One prominent issue arising from these models is their potential to generate harmful content. Traditional moderation systems, typically employing binary classifications (safe vs. unsafe), lack the necessary granularity to distinguish varying levels of harmfulness effectively. This limitation can lead to either excessively restrictive moderation, diminishing user interaction, or inadequate filtering, which could expose users to harmful content. Salesforce AI introduces BingoGuard, an LLM-based moderation system designed to address the inadequacies of binary classification by predicting both binary safety labels and detailed severity levels.

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