Papers by Jingru Fan

2 papers
AgentRM: Enhancing Agent Generalization with Reward Modeling (2025.acl-long)

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Challenge: Existing LLM-based agents have strong performance on held-in tasks, but their generalizability to unseen tasks remains poor.
Approach: They propose a reward-based generalizable reward model to guide the policy model for effective test-time search.
Outcome: The proposed agentRM outperforms existing agents on held-in tasks by 8.8 points on average.
TeachMaster: Generative Teaching via Code (2026.acl-industry)

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Challenge: Existing methods for creating video content are limited by high costs and slow update cycles.
Approach: They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution.
Outcome: The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education.

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