- Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment
Jing Zhao, Ting Zhen, Junwei Bao, Hongfei Jiang, Yang Song · Feb 14, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability.
- DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment
James Wedgwood, Aashiq Muhamed, Mona T. Diab, Virginia Smith · Mar 23, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Preference alignment is usually achieved by weight-updating training on preference data, which adds substantial alignment-stage compute and provides limited mechanistic visibility.
- GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA
Zhichao Wang · Oct 27, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
This paper proposes Group-relative Implicit Fine-Tuning (GIFT), a reinforcement learning framework for aligning large language models (LLMs) that unifies on-policy optimization with implicit preference learning.
- Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents
Xuan Qi · Apr 2, 2026 · Citations: 0
Automatic Metrics
Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly understood.
- PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
Shangjian Yin, Shining Liang, Wenbiao Ding, Yuli Qian, Zhouxing Shi · Oct 8, 2025 · Citations: 0
Pairwise Preference
Despite its small size, fine-tuning Llama-3-8B-Base on PiKa-SFT even outperforms the official Llama-3-8B-Instruct model trained on over 10M proprietary examples on widely used benchmarks such as AlpacaEval 2.0 and Arena-Hard.
- Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty
Yao Xiao, Jung-jae Kim, Roy Ka-wei Lee, Lidong Bing · Oct 7, 2025 · Citations: 0
Pairwise Preference
Self-play preference optimization has emerged as a prominent paradigm for aligning large language models (LLMs).
- SkillX: Automatically Constructing Skill Knowledge Bases for Agents
Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang · Apr 6, 2026 · Citations: 0
Automatic Metrics
Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited…
- The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check
Qingyu Lu, Liang Ding, Kanjian Zhang, Jinxia Zhang, Dacheng Tao · Jan 19, 2026 · Citations: 0
Automatic Metrics
In this work, we present a comprehensive evaluation of dLLMs (e.g., LLaDA, Dream) across two distinct agentic paradigms: Embodied Agents (requiring long-horizon planning) and Tool-Calling Agents (requiring precise formatting).
- Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions
Junhao Su, Yuanliang Wan, Junwei Yang, Hengyu Shi, Tianyang Han · Sep 23, 2025 · Citations: 0
Automatic Metrics
The agent produces a short yet precise reflection: it diagnoses the failure using evidence from the previous step and then proposes a correct, executable follow-up call.
- TARo: Token-level Adaptive Routing for LLM Test-time Alignment
Arushi Rai, Qiang Zhang, Hanqing Zeng, Yunkai Zhang, Dipesh Tamboli · Mar 19, 2026 · Citations: 0
Pairwise Preference
Recent test-time alignment methods offer a lightweight alternative, but have been explored mainly for preference alignment rather than reasoning.
- Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization
Junming Yang, Ning Xu, Biao Liu, Shiqi Qiao, Xin Geng · Sep 27, 2025 · Citations: 0
Pairwise Preference
To bridge this gap, we propose Meta-Weighted Adaptive Preference Optimization (MetaAPO), a novel framework that dynamically couples data generation with model training.
- Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms
Zeguan Xiao, Yun Chen, Guanhua Chen, Ke Tang · Jun 11, 2025 · Citations: 0
Pairwise Preference
Direct Alignment Algorithms (DAAs), such as Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO), have emerged as efficient alternatives to Reinforcement Learning from Human Feedback (RLHF) algorithms for aligning…
- Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis, Seshu Tirupathi, John D. Kelleher · Apr 22, 2026 · Citations: 0
- CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution
Shidong Yang, Ziyu Ma, Tongwen Huang, Yiming Hu, Yong Wang · Apr 17, 2026 · Citations: 0
- C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
Akira Kawabata, Saku Sugawara · Apr 15, 2026 · Citations: 0
- Awakening the Sleeping Agent: Lean-Specific Agentic Data Reactivates General Tool Use in Goedel Prover
Jui-Hui Chung, Hongzhou Lin, Lai Jiang, Shange Tang, Chi Jin · Apr 9, 2026 · Citations: 0
- Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence
Niklas Herbster, Martin Zborowski, Alberto Tosato, Gauthier Gidel, Tommaso Tosato · Apr 9, 2026 · Citations: 0
- Mediocrity is the key for LLM as a Judge Anchor Selection
Shachar Don-Yehiya, Asaf Yehudai, Leshem Choshen, Omri Abend · Mar 17, 2026 · Citations: 0
- PostTrainBench: Can LLM Agents Automate LLM Post-Training?
Ben Rank, Hardik Bhatnagar, Ameya Prabhu, Shira Eisenberg, Karina Nguyen · Mar 9, 2026 · Citations: 0
- Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents
Kaiyu Zhou, Yongsen Zheng, Yicheng He, Meng Xue, Xueluan Gong · Jan 16, 2026 · Citations: 0
- Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
Zouying Cao, Jiaji Deng, Li Yu, Weikang Zhou, Zhaoyang Liu · Dec 11, 2025 · Citations: 0
- FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
Anikait Singh, Sheryl Hsu, Kyle Hsu, Eric Mitchell, Stefano Ermon · Feb 26, 2025 · Citations: 0