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…
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…
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…
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 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…
Baidu has officially open-sourced its latest ERNIE 4.5 series, a powerful family of foundation models designed for enhanced language understanding, reasoning, and generation. The release includes ten model variants ranging from compact 0.3B dense models to massive Mixture-of-Experts (MoE) architectures, with the largest variant totaling 424B parameters. These models are now freely available to the…
Introduction to Generalization in Mathematical Reasoning Large-scale language models with long CoT reasoning, such as DeepSeek-R1, have shown good results on Olympiad-level mathematics. However, models trained through Supervised Fine-Tuning or Reinforcement Learning depend on limited techniques, such as repeating known algebra rules or defaulting to coordinate geometry in diagram problems. Since these models follow learned…
Introduction to Ultra-Long Text Generation Challenges Generating ultra-long texts that span thousands of words is becoming increasingly important for real-world tasks such as storytelling, legal writing, and educational materials. However, large language models still face significant challenges, including length limits and quality issues, as their outputs become increasingly longer. Common problems include incoherence, topic drift,…
LLMs and the Need for Scientific Code Control LLMs have rapidly evolved into complex natural language processors, enabling the development of agentic systems that manage complex workflows. However, the use of LLM agents for generating scientific code is unexplored. Scientific software primarily depends on C++, CUDA, and other low-level languages, which are underrepresented in most…
Large language models have driven progress in machine translation, leveraging massive training corpora to translate dozens of languages and dialects while capturing subtle linguistic nuances. Yet, fine-tuning these models for translation accuracy often impairs their instruction-following and conversational skills, and broad-purpose versions struggle to meet professional fidelity standards. Balancing precise, culturally aware translations with the…