τ-bench: Tool-Agent-User Interaction in Real-World Domains — interactive visual explainer | Rudrite Research Researchers Yao et al. introduced τ-bench, a benchmark for evaluating tool-using AI agents interacting with simulated users, revealing a reliability cliff at pass^k. The benchmark is detailed in a 2024 arXiv paper and is accompanied by a free interactive visual explainer. τ-bench: Tool-Agent-User Interaction in Real-World Domains A benchmark for tool-using agents talking to a simulated user — and the reliability cliff at pass^k. Yao et al. · arXiv 2024 · Reasoning & RL. Read the paper ↗ https://arxiv.org/abs/2406.12045 A free, interactive, animated visual explainer of τ-bench: Tool-Agent-User Interaction in Real-World Domains — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is τ-bench: Tool-Agent-User Interaction in Real-World Domains? - A benchmark for tool-using agents talking to a simulated user — and the reliability cliff at pass^k. - Who published τ-bench: Tool-Agent-User Interaction in Real-World Domains, and where? - Yao et al. — arXiv 2024 arXiv:2406.12045 . - Where can I find a visual explainer of τ-bench: Tool-Agent-User Interaction in Real-World Domains? - Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source. Related explainers DeepSeek-R1 /deepseek-r1 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models /chain-of-thought Training language models to follow instructions with human feedback /instructgpt Direct Preference Optimization: Your Language Model is Secretly a Reward Model /dpo DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models /deepseekmath Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters /test-time-compute Constitutional AI: Harmlessness from AI Feedback /constitutional-ai DAPO: An Open-Source LLM Reinforcement Learning System at Scale /dapo