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Limitations of Reinforcement Learning in Narrow Reasoning Domains Reinforcement Learning RL has demonstrated strong potential to enhance the reasoning capabilities of LLMs, particularly in leading systems such as OpenAI-O3 and DeepSeek-R1. However, most RL research has focused narrowly on math and code, limiting its general applicability. This narrow scope poses two issues: our understanding of how RL improves reasoning may not generalize beyond these domains, and the resulting models often lack versatility. Expanding RL to broader reasoning tasks is challenging due to a lack of reliable reward signals and curated datasets, which are easier to define in mathematical and code-based terms but more difficult in open-ended reasoning domains.  Narrow Domain Focus and Generalization Challenges Reinforcement Learning RL has become a popular method for enhancing the reasoning skills of LLMs, especially after successes with models like OpenAI’s GPT-3 and DeepSeek-R1. Many open-source eff...

In this tutorial, we introduce an advanced AI agent built using Nebius’ robust ecosystem, particularly the ChatNebius, NebiusEmbeddings, and NebiusRetriever components. The agent utilizes the Llama-3.3-70B-Instruct-fast model to generate high-quality responses, incorporating external functionalities such as Wikipedia search, contextual document retrieval, and safe mathematical computation. By combining structured prompt design with LangChain’s modular framework, this tutorial demonstrates how to build a multi-functional, reasoning-capable AI assistant that is both interactive and extensible. Whether for scientific queries, technological insights, or basic numerical tasks, this agent showcases the potential of Nebius as a platform for building sophisticated AI systems. Copy Code Copied Use a different Browser !pip install -q langchain-nebius langchain-core langchain-community wikipedia import os import getpass from typing import List, Dict, Any import wikipedia from dateti...

Google has introduced Gemma 3n, a new addition to its family of open models, designed to bring large multimodal AI capabilities to edge devices. Built from the ground up with a mobile-first design philosophy, Gemma 3n can process and understand text, images, audio, and video on-device, without relying on cloud compute. This architecture represents a significant leap in the direction of privacy-preserving, real-time AI experiences across devices like smartphones, wearables, and smart cameras. Key Technical Highlights of Gemma 3n The Gemma 3n series includes two versions: Gemma 3n E2B and Gemma 3n E4B , optimized to deliver performance on par with traditional 5B and 8B parameter models respectively, while utilizing fewer resources. These models integrate architectural innovations that drastically reduce memory and power requirements, enabling high-quality inference locally on edge hardware. Multimodal Capabilities: Gemma 3n supports multimodal understanding in 35 languages, and tex...

Generative AI and Its Challenges in Autoregressive Code Generation The field of generative artificial intelligence has significantly impacted software development by automating various coding tasks, ranging from simple auto-completions to complex software solutions. However, traditional language models predominantly employ autoregressive methods, predicting one token at a time, which leads to inherent bottlenecks and latency issues. Particularly for coding applications, the slow sequential generation limits efficiency, posing challenges in real-time interactive environments or scenarios demanding immediate responses. Although existing speed-optimized models, such as GPT-4o and Claude 3.5 Haiku, have shown somewhat improved performance, the fundamental constraint of token-by-token generation persists, necessitating a shift toward alternative modeling approaches capable of parallel generation and substantial latency reduction. Current State of AI-Based Coding Assistants and Their Speed...

Modern language agents need to handle multi-turn conversations, retrieving and updating information as tasks evolve. However, most current systems simply add all past interactions to the prompt, regardless of relevance. This leads to bloated memory usage, slower performance, and poor reasoning on longer inputs that weren’t seen during training. Real-world examples, such as research or shopping assistants, show how follow-up questions depend on the previous context. Yet, constant growth prompts strain on system resources and attention. While some solutions use external memory modules, they’re hard to integrate. This raises an important question: can language models learn to manage their memory intelligently as part of reasoning? Limitations of Context-Growing Prompts and Challenges in Memory Integration LLM agents have grown from handling simple queries to navigating complex, multi-step tasks like web browsing and research. Frameworks like ReAct, which blend reasoning and action, have...

Google has unveiled Gemini CLI , an open-source command-line AI agent that integrates the Gemini 2.5 Pro model directly into the terminal. Designed for developers and technical power users, Gemini CLI allows users to interact with Gemini using natural language directly from the command line—supporting workflows such as code explanation, debugging, documentation generation, file manipulation, and even web-grounded research. Gemini CLI builds on the backend infrastructure of Gemini Code Assist and offers a similar intelligence layer to developers who prefer terminal-based interfaces. It supports scripting, prompt-based interactions, and agent extensions, giving developers the flexibility to integrate it into CI/CD pipelines, automation scripts, or everyday development work. By combining terminal accessibility with the full power of Gemini’s multimodal reasoning, Google is positioning this tool as a lightweight but powerful complement to IDE-bound assistants. A standout feature of Gemin...

Why Multimodal Reasoning Matters for Vision-Language Tasks Multimodal reasoning enables models to make informed decisions and answer questions by combining both visual and textual information. This type of reasoning plays a central role in interpreting charts, answering image-based questions, and understanding complex visual documents. The goal is to make machines capable of using vision as humans do—not just seeing but understanding what they see and connecting it to language-based reasoning. Challenges in Visual Reasoning and Language Bias One central challenge in this area is that many models overly depend on linguistic information, even for tasks that require visual interpretation. This reliance leads to performance drops in perception-heavy applications. When a question requires identifying a specific object in an image or interpreting numerical data in a chart, these models often fail because they try to answer using prior language patterns rather than analyzing the visual con...

Beijing Academy of Artificial Intelligence (BAAI) introduces OmniGen2, a next-generation, open-source multimodal generative model. Expanding on its predecessor OmniGen, the new architecture unifies text-to-image generation, image editing, and subject-driven generation within a single transformer framework. It innovates by decoupling the modeling of text and image generation, incorporating a reflective training mechanism, and implementing a purpose-built benchmark—OmniContext—to evaluate contextual consistency. A Decoupled Multimodal Architecture Unlike prior models that use shared parameters across text and image modalities, OmniGen2 introduces two distinct pathways: an autoregressive transformer for text generation and a diffusion-based transformer for image synthesis. It also employs a novel positioning strategy named Omni-RoPE, which allows flexible handling of sequences, spatial coordinates, and modality distinctions, enabling high-fidelity image generation and editing. To prese...

The Challenge: Scaling Autonomous Agents with RL Autonomous AI agents have been at the forefront of taking computational abilities to various real-world tasks, and reinforcement learning (RL) is the key approach in agent creation. RL involves helping computational agents learn by repeatedly interacting with their surroundings, thereby improving their decision-making processes through the use of rewards and penalties. Training agents to self-coordinate in dealing with complex situations involving long-duration interactions, adaptive reasoning, and dynamic information retrieval is challenging. Conventional approaches, based either on supervised data or on strict workflows, cannot deliver generalizable and flexible agents that act effectively in rapidly changing situations, posing serious challenges in developing full-fledged autonomous intelligence. Limitations of Existing Multi-Agent and Supervised Approaches Current agent development methods are grouped into two broad categories, ea...