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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Researchers from Stanford University and Google DeepMind have unveiled Step-Wise Reinforcement Learning (SWiRL), a technique designed to enhance the ability of large language models (LLMs) to tackle complex tasks requiring multi-step reasoning and tool use. As the interest in AI agents and LLM tool use continues to increase, this technique could offer substantial benefits for enterprises looking to integrate reasoning models into their applications and workflows.The challenge of multi-step problemsReal-world enterprise applications often involve multi-step processes. For example, planning a complex marketing campaign may involve market research, internal data analysis, budget calculation and reviewing customer support tickets. This requires online searches, access to internal databases and running code.Traditional reinforcement learning (RL) methods used to fine-tune LLMs, such as Reinforcement Learning from Human Feedback (RLHF) or RL from AI Feedback (RLAIF), typically focus on optimizing models for single-step reasoning tasks. The lead authors of the SWiRL paper, Anna Goldie, research scientist at Google DeepMind, and Azalia Mirhosseini, assistant professor of computer science at Stanford University, believe that current LLM training methods are not suited for the multi-step reasoning tasks that real-world applications require.“LLMs trained via traditional methods typically struggle with multi-step planning and tool integration, meaning that they have difficulty performing tasks that require retrieving and synthesizing documents from multiple sources (e.g., writing a business report) or multiple steps of reasoning and arithmetic calculation (e.g., preparing a financial summary),” they told VentureBeat.Step-Wise Reinforcement Learning (SWiRL)SWiRL tackles this multi-step challenge through a combination of synthetic data generation and a specialized RL approach that trains models on entire sequences of actions. As the researchers state in their paper, “Our goal is to teach the model how to decompose complex problems into a sequence of more manageable subtasks, when to call the tool, how to formulate a call to the tool, when to use the results of these queries to answer the question, and how to effectively synthesize its findings.”SWiRL employs a two-stage methodology. First, it generates and filters large amounts of multi-step reasoning and tool-use data

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Unfortunately for Google, the release of its latest flagship language model, Gemini 2.5 Pro, got buried under the Studio Ghibli AI image storm that sucked the air out of the AI space. And perhaps fearful of its previous failed launches, Google cautiously presented it as “Our most intelligent AI model” instead of the approach of other AI labs, which introduce their new models as the best in the world.However, practical experiments with real-world examples show that Gemini 2.5 Pro is really impressive and might currently be the best reasoning model. This opens the way for many new applications and possibly puts Google at the forefront of the generative AI race. Source: Polymarket. Long context with good coding capabilitiesThe outstanding feature of Gemini 2.5 Pro is its very long context window and output length