Improving the reasoning capabilities of large language models (LLMs) without architectural changes is a core challenge in advancing AI alignment and usability. Researchers at Meta AI and the University of Washington have introduced ASTRO—Autoregressive Search-Taught Reasoner—a novel post-training framework designed to enhance reasoning in Llama-3.1-70B-Instruct. ASTRO is unique in teaching models to perform in-context search, […] The post Can We Improve Llama 3’s Reasoning Through Post-Training Alone? ASTRO Shows +16% to +20% Benchmark Gains appeared first on MarkTechPost. read more

Reward models are fundamental components for aligning LLMs with human feedback, yet they face the challenge of reward hacking issues. These models focus on superficial attributes such as response length or formatting rather than identifying true quality indicators like factuality and relevance. This problem arises because standard training objectives fail to differentiate between spurious correlations […] The post Crome: Google DeepMind’s Causal Framework for Robust Reward Modeling in LLM Alignment appeared first on MarkTechPost. read more

When we first land in the Codex environment, it feels like stepping into a co-pilot’s seat for coding. Codex is designed to take over much of the routine or overwhelming parts of software engineering, like understanding massive codebases, drafting PRs, and finding bugs, and help us focus on higher-level thinking. In this guided setup, we […] The post A Tutorial on Using OpenAI Codex with GitHub Repositories for Seamless AI-Powered Development appeared first on MarkTechPost. read more

Understanding the Limits of Current Interpretability Tools in LLMs AI models, such as DeepSeek and GPT variants, rely on billions of parameters working together to handle complex reasoning tasks. Despite their capabilities, one major challenge is understanding which parts of their reasoning have the greatest influence on the final output. This is especially crucial for […] The post Thought Anchors: A Machine Learning Framework for Identifying and Measuring Key Reasoning Steps in Large Language Models with Precision appeared first on MarkTechPost. read more

In this tutorial, we implement the BioCypher AI Agent, a powerful tool designed for building, querying, and analyzing biomedical knowledge graphs using the BioCypher framework. By combining the strengths of BioCypher, a high-performance, schema-based interface for biological data integration, with the flexibility of NetworkX, this tutorial empowers users to simulate complex biological relationships such as […] The post Building a BioCypher-Powered AI Agent for Biomedical Knowledge Graph Generation and Querying appeared first on MarkTechPost. read more

TNG Technology Consulting has unveiled DeepSeek-TNG R1T2 Chimera, a new Assembly-of-Experts (AoE) model that blends intelligence and speed through an innovative model merging strategy. Built from three high-performing parent models—R1-0528, R1, and V3-0324—R1T2 demonstrates how expert-layer interpolation at scale can unlock new efficiencies in large language models (LLMs). Assembly-of-Experts: Efficient Model Composition at Scale Traditional […] The post DeepSeek R1T2 Chimera: 200% Faster Than R1-0528 With Improved Reasoning and Compact Output appeared first on MarkTechPost. read more

Together AI has released DeepSWE, a state-of-the-art, fully open-sourced software engineering agent that is trained entirely through reinforcement learning (RL). Built on top of the Qwen3-32B language model, DeepSWE achieves 59% accuracy on the SWEBench-Verified benchmark and 42.2% Pass@1, topping the leaderboard among open-weight models. This launch represents a significant shift for Together AI, from […] The post Together AI Releases DeepSWE: A Fully Open-Source RL-Trained Coding Agent Based on Qwen3-32B and Achieves 59% on SWEBench appeared first on MarkTechPost. read more

Introduction: Reinforcement Learning Progress through Chain-of-Thought Prompting LLMs have shown excellent progress in complex reasoning tasks through CoT prompting combined with large-scale reinforcement learning (RL). Models like Deepseek-R1-Zero have shown strong reasoning capabilities by applying RL directly to base models. Similarly, methods such as SimpleRL and Open-ReasonerZero show improvements in smaller models like the Qwen […] The post Shanghai Jiao Tong Researchers Propose OctoThinker for Reinforcement Learning-Scalable LLM Development appeared first on MarkTechPost. read more

Understanding the Role of Chain-of-Thought in LLMs Large language models are increasingly being used to solve complex tasks such as mathematics and scientific reasoning through structured chain-of-thought approaches. These models do not just jump to answers—they reason through intermediate steps that simulate logical thought processes. This technique allows for improved reasoning accuracy and clearer error […] The post ReasonFlux-PRM: A Trajectory-Aware Reward Model Enhancing Chain-of-Thought Reasoning in LLMs appeared first on MarkTechPost. read more

The Need for Cognitive and Adaptive Search Engines Modern search systems are evolving rapidly as the demand for context-aware, adaptive information retrieval grows. With the increasing volume and complexity of user queries, particularly those requiring layered reasoning, systems are no longer limited to simple keyword matching or document ranking. Instead, they aim to mimic the […] The post Baidu Researchers Propose AI Search Paradigm: A Multi-Agent Framework for Smarter Information Retrieval appeared first on MarkTechPost. read more