REVIEW 10 cited by
Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The vision of a broadly capable and goal-directed agent, such as an Internet-browsing agent in the digital world and a household humanoid in the physical world, has rapidly advanced, thanks to the generalization capability of foundation models. Such a generalist agent needs to have a large and diverse skill repertoire, such as finding directions between two travel locations and buying specific items from the Internet. If each skill needs to be specified manually through a fixed set of human-annotated instructions, the agent's skill repertoire will necessarily be limited due to the quantity and diversity of human-annotated instructions. In this work, we address this challenge by proposing Proposer-Agent-Evaluator, an effective learning system that enables foundation model agents to autonomously discover and practice skills in the wild. At the heart of PAE is a context-aware task proposer that autonomously proposes tasks for the agent to practice with context information of the environment such as user demos or even just the name of the website itself for Internet-browsing agents. Then, the agent policy attempts those tasks with thoughts and actual grounded operations in the real world with resulting trajectories evaluated by an autonomous VLM-based success evaluator. The success evaluation serves as the reward signal for the agent to refine its policies through RL. We validate PAE on challenging vision-based web navigation, using both real-world and self-hosted websites from WebVoyager and WebArena.To the best of our knowledge, this work represents the first effective learning system to apply autonomous task proposal with RL for agents that generalizes real-world human-annotated benchmarks with SOTA performances. Our open-source checkpoints and code can be found in https://yanqval.github.io/PAE/
Forward citations
Cited by 10 Pith papers
-
Prismata: Confining Cross-Site Prompt Injection in Web Agents
Prismata cuts web-agent prompt-injection attack success from 85.5% to 0.7% via Biba-inspired DOM trust labeling and mechanical least-privilege confinement without site annotations.
-
RMSWeb: Reflection, Failure-Mode Mining, and Salvage-DS for Web Agent Reinforcement Learning
RMSWeb combines reflection-conditioned collection, failure-mode mining, and a competence-gated offline RL objective to improve compact web agents' success and step efficiency on live-web benchmarks.
-
WebGuard: Building a Generalizable Guardrail for Web Agents
WebGuard introduces an action-level risk dataset for web agents and shows that a fine-tuned 7B model improves risk-prediction accuracy from about 38% to 80% and high-risk recall from 20% to 76%, still below deployment...
-
WebSynthesis: World-Model-Guided MCTS for Efficient WebUI-Trajectory Synthesis
A world-model-guided MCTS pipeline synthesizes 4k web navigation trajectories and yields a WebArena Pass@3 success rate of 20.15%, above OS-Genesis (18.66%) and AgentTrek (11.94%).
-
Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
Scaling the number of interaction steps, trained via a curriculum over rollout horizon, improves web-agent task success and outperforms scaling per-step reasoning under fixed token budgets.
-
Self-Challenging Language Model Agents
A language model agent can generate its own verifiable training tasks and improve its tool-use success rate by about 2x without human-annotated data.
-
ProgRM: Build Better GUI Agents with Progress Rewards
ProgRM, a per-step progress reward model trained with LCS-based self-annotated labels, improves RL-trained GUI agent success rates on WikiHow relative to outcome reward models.
-
Rethinking Agent Design: From Top-Down Workflows to Bottom-Up Skill Evolution
Agents that start with no game knowledge can build a reusable skill library through trial-and-error and visual feedback, then progress further in two complex games than baseline agents given extra hints.
-
InSTA: Towards Internet-Scale Training For Agents
Automated LLM task generation, agent execution, and judge filtering at 150k-site scale lets a 1.7B model match much larger web agents.
-
Automated Skill Discovery for Language Agents through Exploration and Iterative Feedback
EXIF repeatedly has a teacher agent explore an environment, relabel the exploration as tasks, train a student agent on it, and use the student's failures to guide the next round, improving 7B-8B agents in Webshop and Crafter.
Discussion (0). Continue with ORCID to comment.