The article presents rStar-Math, a method demonstrating that small language models (SLMs) can rival or surpass the math reasoning capabilities of larger models like OpenAI's without distillation. rStar-Math employs Monte Carlo Tree Search (MCTS) for 'deep thinking', using a math policy SLM guided by an SLM-based process reward model. It introduces three innovations: a code-augmented CoT data synthesis method for training the policy SLM, a novel process reward model training method avoiding step-level score annotation, and a self-evolution recipe where both the policy SLM and process preference model are iteratively improved. Through self-evolution with millions of solutions for 747k math problems, rStar-Math achieves state-of-the-art math reasoning, significantly improving performance on benchmarks like MATH and AIME.
This article explores QwQ-32B-Preview, an experimental AI model by Qwen Team, which focuses on advancing AI reasoning capabilities. It discusses the model's performance, limitations, and its deep contemplative abilities on various benchmarks and problems.
A Python hands-on guide to understand the principles of generating new knowledge by following logical processes in knowledge graphs. Discusses the limitations of LLMs in structured reasoning compared to the rigorous logical processes needed in certain fields.
“we found no evidence of formal reasoning in language models …. Their behavior is better explained by sophisticated pattern matching—so fragile, in fact, that changing names can alter results by ~10%!”
This article provides a comprehensive overview of AI agents, discussing their core traits, technical aspects, and practical applications. It covers topics like autonomy, reasoning, alignment, and the role of AI agents in daily life.
1. **Emerging Prominence of AI Agents**: Agents are increasingly popular for day-to-day tasks but come with confusion about their definition and effective use.
2. **Core Traits and Autonomy**: Julia Winn explores the nuances of AI agents' autonomy and proposes a spectrum of agentic behavior to assess their suitability.
3. **AI Alignment and Safety**: Tarik Dzekman discusses the challenges of aligning AI agents with creators' goals, particularly focusing on safety and unintended consequences.
4. **Tool Calling and Reasoning**: Tula Masterman examines how AI agents bridge tool use with reasoning and the challenges they face in tool calling.
5. **Proprietary vs. Open-Source AI**: Gadi Singer compares the advantages and limitations of proprietary and open-source AI products for implementing agents.
1. **Emerging Prominence of AI Agents**: Agents are increasingly popular for day-to-day tasks but come with confusion about their definition and effective use.
2. **Core Traits and Autonomy**: Julia Winn explores the nuances of AI agents' autonomy and proposes a spectrum of agentic behavior to assess their suitability.
3. **AI Alignment and Safety**: Tarik Dzekman discusses the challenges of aligning AI agents with creators' goals, particularly focusing on safety and unintended consequences.
4. **Tool Calling and Reasoning**: Tula Masterman examines how AI agents bridge tool use with reasoning and the challenges they face in tool calling.
5. **Proprietary vs. Open-Source AI**: Gadi Singer compares the advantages and limitations of proprietary and open-source AI products for implementing agents.
The article discusses the limitations of Large Language Models (LLMs) in planning and self-verification tasks, and proposes an LLM-Modulo framework to leverage their strengths in a more effective manner. The framework combines LLMs with external model-based verifiers to generate, evaluate, and improve plans, ensuring their correctness and efficiency.
"Simply put, we take the stance that LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for human or machine agents, if rightly used."
"Simply put, we take the stance that LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for human or machine agents, if rightly used."
