Researchers from Google Research and the University of Toronto have developed a zero-shot agent for autonomous learning and task execution in live computer environments. The agent, built on top of PaLM2, a large language model, uses a single set of instruction prompts for all activities and demonstrates high task completion rates on the MINIWOB++ benchmark. The agent’s design eliminates the need for expert traces as guidance, opening up possibilities for independent learning and enhancing control over computers.
**Large language models (LLMs) for action production in various live contexts have shown promise in earlier efforts. LLMs are used to follow expert trails, understand environmental changes, plan and carry out future activities, and compose API requests. Several studies have demonstrated that repeatedly performing a task with self-reflection can significantly enhance task completion.**
**Recently, MINIWOB++ has been utilized as a testbed to evaluate LLM’s performance on computing workloads. The challenge is to enable an agent to independently know and enhance its control over a computer without utilizing expert traces as guidance. Researchers from Google and the University of Toronto have developed a zero-shot agent to address this challenge.**
**Their agent, built on top of PaLM2, uses a single set of instruction prompts for all activities, rather than task-specific prompts. The key is to provide condensed screen depictions and a straightforward action planner that plans out executable operations on a state. They have demonstrated that this approach can complete nearly all simple tasks on the MINIWOB++ benchmark.**
**To help the agent learn from exploratory failures and advance in more difficult tasks, they suggest a systematic thought management technique. Their agent achieves performance equivalent to previous few/many-shot state-of-the-art after a few rounds of tries. It is the first zero-shot design for computer control tasks known to the research community.**
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