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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,…
We’re exploring the frontiers of AGI, prioritizing readiness, proactive risk assessment, and collaboration with the wider AI community. Artificial general intelligence (AGI), AI that’s at least as capable as humans at most cognitive tasks, could be here within the coming years. Integrated with agentic capabilities, AGI could supercharge AI to understand, reason, plan, and execute…
I’m definitely not the only person who feels that YouTube sponsor segments have become longer and more frequent recently. Sometimes, I watch videos that seem to be trying to sell me something every couple of seconds.
On one hand, it’s great that both small and medium-sized YouTubers are able to make a living from their…
Recent advancements in AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by models like OpenAI o1 and DeepSeek R1, enhances model performance by leveraging additional computational resources during inference. Test-time budget forcing has emerged as an efficient technique in LLMs, enabling improved performance…
Last updated March 26 Today we’re introducing Gemini 2.5, our most intelligent AI model. Our first 2.5 release is an experimental version of 2.5 Pro, which is state-of-the-art on a wide range of benchmarks and debuts at #1 on LMArena by a significant margin. Gemini 2.5 models are thinking models, capable of reasoning through their…
In my previous article, I discussed how morphological feature extractors mimic the way biological experts visually assess images.
This time, I want to go a step further and explore a new question: Can different architectures complement each other to build an AI that “sees” like an expert?
Introduction: Rethinking Model Architecture Design
While building a…
Recent advancements in artificial intelligence have significantly improved how machines learn to associate visual content with language. Contrastive learning models have been pivotal in this transformation, particularly those aligning images and text through a shared embedding space. These models are central to zero-shot classification, image-text retrieval, and multimodal reasoning. However, while these tools have pushed…
In December we first introduced native image output in Gemini 2.0 Flash to trusted testers. Today, we're making it available for developer experimentation across all regions currently supported by Google AI Studio. You can test this new capability using an experimental version of Gemini 2.0 Flash (gemini-2.0-flash-exp) in Google AI Studio and via the Gemini…
Designing imitation learning (IL) policies involves many choices, such as selecting features, architecture, and policy representation. The field is advancing quickly, introducing many new techniques and increasing complexity, making it difficult to explore all possible designs and understand their impact. IL enables agents to learn through demonstrations rather than reward-based approaches. The increasing number of…