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benchmark with an 89.1% success rate. Co-founder, CTO Method; The Results; 🤔 Do these results matter?; Analysis; Manual correction of evaluations; What's Next?; Want to build Web Agents? We're excited to announce that Browser Use has achieved state-of-the-art performance on the WebVoyager benchmark, with an impressive 89.1% success rate across 586 diverse web tasks. And the best part? We are FULLY open source (repo). Web Agent Accuracy Method We took the existing WebVoyager codebase and slightl

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tasks; Reward hacking examples in real life; Why does Reward Hacking Exist?; Hacking RL Environment; Hacking RLHF of LLMs Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task. Reward hacking exists because RL environments are often imperfect, and it is fundamentally challenging to accurately specify a reward function. With the rise of language models

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the literature . PaperQA2's high-accuracy design goal results in a different implementation from other commercial RAG systems. What we found to be important for RAG accuracy: ‍ An agentic approach, allowing for iterative query expansion. LLM re-ranking and contexutal summarization (RCS), exchanging more compute for higher accuracy. Document citation traversal, for higher retrieval recall beyond keyword search. What we found to be unimportant for RAG accuracy: (when using RCS) Embedding model cho

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an automated system that can synthesize cited Wikipedia-style summaries for technical topics from the scientific literature. WikiCrow is built on top of FutureHouse’s internal LLM agent platform, PaperQA, which in our testing, achieves state-of-the-art (SOTA) performance on a retrieval-focused version of PubMedQA and other benchmarks, including a new retrieval-first benchmark, LitQA, developed internally to evaluate systems retrieving full-text PDFs across the entire scientific literature. As a

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rning Progress Today, OpenAI released a new result. We used PPO (Proximal Policy Optimization), a general reinforcement learning algorithm invented by OpenAI, to train a team of 5 agents to play Dota and beat semi-pros. This is the game that to me feels closest to the real world and complex decision making (combining strategy, tactics, coordinating, and real-time action) of any game AI had made real progress against so far. The agents we train consistently outperform two-week old agents with a w

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