From Trajectories to Grounded Preferences: Process Preference Synthesis via Interaction Element Graphs for Web PRMs

arXiv:2609.32351 · cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/PoloWitty/surfPRM

Terminology

• Accuracy — 22× in this paper · explained in 1 episode
• Verification — 13× in this paper · explained in 2 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Affordance — 11× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Meeting of the Association for Computational Linguistics — 7× in this paper
• Hallucination — 5× in this paper · explained in 3 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Generative AI — 4× in this paper · explained in 2 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 4× in this paper
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Credit assignment — 3× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 3× in this paper
• Process Reward Models (PRMs) — 3× in this paper · explained in 3 episodes
• Verifier — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• alpha — 2× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Impact of the Grounded Minimal Contrastive Pair — 1× in this paper
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• The Llama 3 Herd of Models
• VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
• Qwen2.5 Technical Report
• OpenAI GPT-5 System Card
• Solving math word problems with process- and outcome-based feedback
• Qwen3 Technical Report

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