Existing prompting and supervised fine-tuning (SFT) methods remain fixed by prompt rules or training corpora, and are usually benchmarked only on wellstructured wiki sources, limiting real-world adaptability. We introduce WebPuzzle, a 24k-sample training and 275-sample test benchmark that evaluates information seeking on the live internet, across both wiki and open-domain queries. Leveraging 7k WebPuzzle instances, we develop DeepDiver, a reinforcement-learning (RL) framework that cultivates Search Intensity Scaling (SIS)--an emergent ability to escalate search frequency and depth instead of settling on overconfident, underevidenced answers. With SIS, Qwen2.5-7B-Instruct and Pangu-7B-Reasoner attain performance on real-web tasks comparable to the 671B-parameter DeepSeek-R1. We detail DeepDiver's curriculum from cold-start SFT to a well designed RL procedure, and show that its seeking policy generalized from closed-ended queries to open-ended generation such as long-form writing. Our results advance adaptive information seeking in LLMs and provide a rigorous benchmark for future work.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. A year ago, during his rookie Formula One campaign, Kimi Antonelli, the 19-year-old Italian driving prodigy tapped to replace seven-time champion Lewis Hamilton in the Mercedes lineup, spent the days after his first podium finish completing his final high school exams. This season, schoolwork in the rearview mirror, Antonelli can't stop winning and setting new records.

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In this section we prove the theoretical results around the dual curriculum game and use these results to show approximation bounds for our methods, given that they have reached a Nash equilibrium (NE). The first theorem is the main result that allows us to analyze dual curriculum games. The high-level result says that the NE of a dual curriculum game are approximate NE of the base game from the perspective of any of the individual players, or from the perspective of the joint strategy. Let Bbe the maximum difference between U1t and U2t, and let (π,θ1,θ2) be a NE for G. Then (π,pθ1 + (1 p)θ2) is an approximate NE for the base game with either teacher or for a teacher optimizing their joint objective. More precisely, it is a 2Bp(1 p)-approximate NE when Ut = pU1t + (1 p)U2t, a 2B(1 p)-approximate NE when Ut = U1t, and a 2Bp-approximate NE when Ut = U2t. At a high level, this is true because, for low values of p, the best-response strategies for the individual players can be thought of as approximate-best response strategies for the joint-player, and vis-versa. Since the Nash Equilibrium consists of each of the players playing their own best response, they must be playing an approximate best response for the joint-player. We provide a formal proof below: Proof. Let B be the maximum difference between U1t and U2t, and let (π,θ1,θ2) be a Nash Equilibrium for G. Then consider pθ1 + (1 p)θ2 as a strategy in the base game for the joint player pU1t + (1 p)U2t.

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Claire chatted to Jamie Palmer from Icarus Robotics about building a robotic labour force to perform routine and risky tasks in orbit. Jamie Palmer is co-founder and CTO of Icarus Robotics . He earned a Master's in Robotics from Columbia University on a full scholarship, researching intelligent, dexterous manipulation in the ROAM lab. Jamie developed and deployed autonomous hospital robots during the pandemic and worked as a race-winning engineer for the Mercedes-AMG Petronas Formula One team. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Let B be the maximum difference betweenU1t and U2t, and let (π,θ1,θ2) be a Nash Equilibrium forG. Let π1 be the best response to the first teacher (with utilityU1t) and let π1+2 be the best response policy to the joint teacher. This result shows that as we reduce the number of random episodes, the approximation to aminimax regret strategy improves. Let G be the dual curriculum game in which the first teacher maximizes regret, so U1t = URt, and the second teacher plays randomly, soU2t = UUt . Finally,we need to show thatπ2+3 isoptimal for the student.