PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing LLM agents generate verbose and inefficient natural language plans to guide reasoning, which restricts agents’ ability to generalize across similar tasks. |
| Approach: | They propose a pseudocode-style planning guide optimization method that captures the structural logic of reasoning and uses two planning-oriented rewards to enhance agent learning. |
| Outcome: | The proposed method outperforms existing LLM agents on representative agent benchmarks and outperformed the current leading baselines. |
Similar Papers
Teaching LLMs to Plan, Not Just Solve: Plan Learning Boosts LLMs Generalization in Reasoning Tasks (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for reinforcement learning (RL) on self-generated data are limited in many domains. |
| Approach: | a new framework combines plan-based search with Step-level Advantage Preference Optimization to optimize plan learning. |
| Outcome: | The proposed framework improves in-domain performance and out-of-domain benchmarks. |
Personal Large Language Model Agents: A Case Study on Tailored Travel Planning (2024.emnlp-industry)
Copied to clipboard
Harmanpreet Singh, Nikhil Verma, Yixiao Wang, Manasa Bharadwaj, Homa Fashandi, Kevin Ferreira, Chul Lee
| Challenge: | Large Language Models (LLMs) are becoming more autonomous and capable of handling real-world tasks through their access to tools, various planning strategies, and memory, referred to as LLM agents. |
| Approach: | They introduce a personalized version of TravelPlanner and establish baselines for personal LLM agents by comparing generic and personal plans. |
| Outcome: | The proposed model encapsulates user-related information, preferences, and personal concepts and provides baselines for personal LLM agents. |
MPO: Boosting LLM Agents with Meta Plan Optimization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for interactive planning tasks suffer from planning hallucinations and require retraining for each new agent. |
| Approach: | They propose a framework that leverages explicit guidance through meta plans to assist agent planning and enables continuous optimization based on feedback from the agent’s task execution. |
| Outcome: | The proposed framework outperforms existing baselines on two representative tasks and significantly improves task completion efficiency and generalization capabilities. |
Reasoning with Language Model is Planning with World Model (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. |
| Approach: | They propose a framework that repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) |
| Outcome: | The proposed framework repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) it achieves optimum balance between exploration and exploitation, while achieving high-reward reasoning paths efficiently. |
InfoPO: On Mutual Information Maximization for Large Language Model Alignment (2025.naacl-long)
Copied to clipboard
Teng Xiao, Zhen Ge, Sujay Sanghavi, Tian Wang, Julian Katz-Samuels, Marc Versage, Qingjun Cui, Trishul Chilimbi
| Challenge: | Recent studies have shown that direct preference optimization and its variants can be useful for fine-tuning large language models with human preferences data. |
| Approach: | They propose a preference fine-tuning algorithm that effectively and efficiently aligns large language models using preference data. |
| Outcome: | Extensive experiments show that the proposed algorithm outperforms established baselines on reasoning tasks. |
SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for aligning Large Language Models with human values are limited and results of DPO are not resilient. |
| Approach: | They propose a self-guided direct preference optimization algorithm that incorporates a pilot term to steer the gradient flow during the optimization process. |
| Outcome: | The proposed method can generate human-preferred response up to 9.19% higher than previous methods. |
Probability-Consistent Preference Optimization for Enhanced LLM Reasoning (2025.findings-acl)
Copied to clipboard
Yunqiao Yang, Houxing Ren, Zimu Lu, Ke Wang, Weikang Shi, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li
| Challenge: | Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models. |
| Approach: | They propose a framework that establishes two quantitative metrics for preference selection: surface-level answer correctness and intrinsic token-level probability consistency. |
| Outcome: | The proposed framework outperforms existing outcome-only criterion approaches across a diverse range of LLMs and benchmarks. |
MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)
Copied to clipboard
Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng
| Challenge: | Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data. |
| Approach: | They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data. |
| Outcome: | The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard. |
K-order Ranking Preference Optimization for Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing list-wise methods focus on optimizing list ranking consistency for LLMs to improve ranking abilities. |
| Approach: | They propose to extend the Plackett-Luce model to accommodate top-K ranking by extending the DPO’s Plact-Lucer model to dynamically determine appropriate K for different samples. |
| Outcome: | The proposed model can be extended to accommodate top-K ranking and improve training efficiency. |
DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage. |
| Approach: | They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents. |
| Outcome: | Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget. |