Papers by Geon-Hyeong Kim

2 papers
Prospector: Improving LLM Agents with Self-Asking and Trajectory Ranking (2024.findings-emnlp)

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Challenge: Existing LLMs are limited in their ability to incorporate feedback from an environment.
Approach: They propose an LLM agent that consists of an Actor and a Critic.
Outcome: The proposed agent outperforms existing LLMs on benchmark environments and shows that it can generate diverse trajectories and pick the most rewarding trajectory.
IRPO: Implicit Policy Regularized Preference Optimization (2026.findings-eacl)

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Challenge: Recent DPOs introduce additional hyperparameters, reducing feasibility for LLM fine-tuning.
Approach: They propose an algorithm that regularizes the reward against a reference policy without extra hyperparameters to address suboptimal outcomes.
Outcome: The proposed algorithm outperforms baseline algorithms with the same hyperparameter complexity while maintaining training simplicity.

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