Hugging Face Trending Papers
Discriminative World Models for Web Agents
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arXiv AI
6d ago
Discriminative World Models for Web Agents
The paper introduces Discriminative World Models for Web Agents, proposing a predicted-state matching objective that trains world models to produce representations that can distinguish the true resulting state from those of alternative actions. Using a branching dataset from WebArena Go-Browse, the authors demonstrate that this approach outperforms traditional supervised next-state prediction on a held‑out benchmark and improves action ranking on WebPRMBench. Additionally, employing the discriminative world model for test‑time action selection boosts end‑to‑end task success on WebArena‑Lite.
arXiv Machine Learning
6d ago
Beyond State Consistency: Behavior Consistency in Text-Based World Models
The paper introduces a new training paradigm for text-based world models that prioritizes behavior consistency over traditional state consistency metrics. It proposes the Behavior Consistency Reward (BehR), a step-level metric that evaluates how the likelihood of a logged next action changes between real and predicted states using a frozen Reference Agent. Experiments on WebShop and TextWorld demonstrate that BehR-based training improves long-term alignment, reduces false positives in offline evaluation, and yields modest gains in lookahead planning while maintaining or enhancing single-step prediction quality.
arXiv Machine Learning
Aug 4
RMSWeb: Reflection, Failure-Mode Mining, and Salvage-DS for Web Agent Reinforcement Learning
arXiv:2608. 00335v1 Announce Type: cross Abstract: Compact web agents can reduce deployment cost, but training them poses challenges in both data collection and post-SFT reinforcement learning (RL).
arXiv AI
Jun 10
3SPO: State-Score-Supervised Policy Optimization for LLM Agents
arXiv:2606. 09961v1 Announce Type: cross Abstract: Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performance in long-horizon tasks.
arXiv Machine Learning
Jul 31
TAPO: Transition-Aware Policy Optimization for LLM Agents
arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.
arXiv Machine Learning
Jun 25
ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning
arXiv:2501. 14622v5 Announce Type: replace Abstract: Learning efficient representations for decision-making policies is a challenge in imitation learning (IL).