- 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.
- $V_1$: Unifying Generation and Self-Verification for Parallel Reasoners
Harman Singh, Xiuyu Li, Kusha Sareen, Monishwaran Maheswaran, Sijun Tan · Mar 4, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
On code generation (LiveCodeBench, CodeContests, SWE-Bench) and math reasoning (AIME, HMMT) benchmarks, V_1-Infer improves Pass@1 by up to 10% over pointwise verification and outperforms recent test-time scaling methods while being…
- KLong: Training LLM Agent for Extremely Long-horizon Tasks
Yue Liu, Yingwei Ma, Yibo Miao, Yanhao Li, Yuchong Xie · Feb 19, 2026 · Citations: 0
Rubric Rating
Then, we introduce Research-Factory, an automated pipeline that generates high-quality training data by collecting research papers and constructing evaluation rubrics.
- 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.
- S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models
Jack Young · Apr 1, 2026 · Citations: 0
Automatic Metrics
Using roughly 48 execution-verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval.
- SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents
Patrick Tser Jern Kon, Archana Pradeep, Ang Chen, Alexander P. Ellis, Warren Hunt · Feb 25, 2026 · Citations: 0
Automatic Metrics
Our approach combines supervised fine-tuning on expert-augmented trajectories with agentic reinforcement learning that explicitly discourages degenerative looping and unproductive expert collaboration.
- Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs
Ngoc Bui, Shubham Sharma, Simran Lamba, Saumitra Mishra, Rex Ying · Dec 3, 2025 · Citations: 0
Automatic Metrics
Across mathematical reasoning (GSM8K, MATH-500, AIME24), procedural generation (LongProc), conversational long-memory benchmarks (LongMemEval), and long-context understanding (LongBenchV2 and SCBench), TRIM-KV consistently outperforms…
- 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).
- Structurally Aligned Subtask-Level Memory for Software Engineering Agents
Kangning Shen, Jingyuan Zhang, Chenxi Sun, Wencong Zeng, Yang Yue · Feb 25, 2026 · Citations: 0
Automatic Metrics
Large Language Models (LLMs) have demonstrated significant potential as autonomous software engineering (SWE) agents.
- DeepPrune: Parallel Scaling without Inter-trace Redundancy
Shangqing Tu, Yaxuan Li, Yushi Bai, Lei Hou, Juanzi Li · Oct 9, 2025 · Citations: 0
Llm As JudgeAutomatic Metrics
Our method features a specialized judge model trained with out-of-distribution data (AIME 2022, AIME 2023, and MATH 500) using oversampling techniques to accurately predict answer equivalence from partial reasoning traces, achieving 0.7072…
- Cross-Context Verification: Hierarchical Detection of Benchmark Contamination through Session-Isolated Analysis
Tae-Eun Song · Mar 23, 2026 · Citations: 0
Automatic Metrics
LLM coding benchmarks face a credibility crisis: widespread solution leakage and test quality issues undermine SWE-bench Verified, while existing detection methods--paraphrase consistency, n-gram overlap, perplexity analysis--never directly…
- Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models
Teng Wang, Zhangyi Jiang, Zhenqi He, Shenyang Tong, Wenhan Yang · Mar 16, 2025 · Citations: 0
Automatic Metrics
Empirical results on the PRM800K dataset show that HRM, together with HNC, provides more stable and reliable evaluations than PRM.
- 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…
- C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
Akira Kawabata, Saku Sugawara · Apr 15, 2026 · Citations: 0
- Peer-Predictive Self-Training for Language Model Reasoning
Shi Feng, Hanlin Zhang, Fan Nie, Sham Kakade, Yiling Chen · Apr 14, 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
- SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling
Yiqi Zhang, Huiqiang Jiang, Xufang Luo, Zhihe Yang, Chengruidong Zhang · Mar 24, 2026 · Citations: 0
- AdaRubric: Task-Adaptive Rubrics for LLM Agent Evaluation
Liang Ding · Mar 22, 2026 · Citations: 0
- FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair
Ruize Ma, Yilei Jiang, Shilin Zhang, Zheng Ma, Yi Feng · Mar 18, 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
- daVinci-Env: Open SWE Environment Synthesis at Scale
Dayuan Fu, Shenyu Wu, Yunze Wu, Zerui Peng, Yaxing Huang · Mar 13, 2026 · Citations: 0
- TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya, Michael Gastpar, Ashok Vardhan Makkuva · Mar 13, 2026 · Citations: 0
- Tool Verification for Test-Time Reinforcement Learning
Ruotong Liao, Nikolai Röhrich, Xiaohan Wang, Yuhui Zhang, Yasaman Samadzadeh · Mar 2, 2026 · Citations: 0
- Qwen3-Coder-Next Technical Report
Ruisheng Cao, Mouxiang Chen, Jiawei Chen, Zeyu Cui, Yunlong Feng · Feb 28, 2026 · Citations: 0
- Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs
Jie Cao, Tianwei Lin, Zhenxuan Fan, Bo Yuan, Ziyuan Zhao · Feb 28, 2026 · Citations: 0
- SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale
Ibragim Badertdinov, Maksim Nekrashevich, Anton Shevtsov, Alexander Golubev · Feb 27, 2026 · Citations: 0
- Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents
Zhi Chen, Zhensu Sun, Yuling Shi, Chao Peng, Xiaodong Gu · Feb 8, 2026 · Citations: 0
- TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks
Vansh Kapoor, Aman Gupta, Hao Chen, Anurag Beniwal, Jing Huang · Jan 15, 2026 · Citations: 0
- PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models
Haoyu Zheng, Yun Zhu, Yuqian Yuan, Bo Yuan, Wenqiao Zhang · Jan 7, 2026 · Citations: 0
- SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang · Oct 10, 2025 · Citations: 0
- Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, Jeff Clune · May 29, 2025 · Citations: 0
- Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarios
Zhi Chen, Wei Ma, Lingxiao Jiang · Mar 16, 2025 · Citations: 0