Saved in:
| Main Authors: | Wu, Yangzhen, Li, Shanda, Wen, Zixin, Zhou, Xin, Talwalkar, Ameet, Yang, Yiming, Huang, Wenhao, Cai, Tianle |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01223 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
by: Li, Shanda, et al.
Published: (2025)
by: Li, Shanda, et al.
Published: (2025)
CoMind: Towards Community-Driven Agents for Machine Learning Engineering
by: Li, Sijie, et al.
Published: (2025)
by: Li, Sijie, et al.
Published: (2025)
FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization
by: Feng, Shengyu, et al.
Published: (2025)
by: Feng, Shengyu, et al.
Published: (2025)
Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
by: Wu, Yangzhen, et al.
Published: (2024)
by: Wu, Yangzhen, et al.
Published: (2024)
Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection
by: Li, Sijie, et al.
Published: (2026)
by: Li, Sijie, et al.
Published: (2026)
Sample Complexity and Representation Ability of Test-time Scaling Paradigms
by: Huang, Baihe, et al.
Published: (2025)
by: Huang, Baihe, et al.
Published: (2025)
Do LLMs exhibit human-like response biases? A case study in survey design
by: Tjuatja, Lindia, et al.
Published: (2023)
by: Tjuatja, Lindia, et al.
Published: (2023)
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization
by: Sun, Weiwei, et al.
Published: (2025)
by: Sun, Weiwei, et al.
Published: (2025)
Provably tuning the ElasticNet across instances
by: Balcan, Maria-Florina, et al.
Published: (2022)
by: Balcan, Maria-Florina, et al.
Published: (2022)
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
by: Kolawole, Steven, et al.
Published: (2024)
by: Kolawole, Steven, et al.
Published: (2024)
ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data
by: Shen, Junhong, et al.
Published: (2024)
by: Shen, Junhong, et al.
Published: (2024)
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
by: Luo, Yun, et al.
Published: (2023)
by: Luo, Yun, et al.
Published: (2023)
Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets
by: Yang, Yuchen, et al.
Published: (2026)
by: Yang, Yuchen, et al.
Published: (2026)
Comparing Developer and LLM Biases in Code Evaluation
by: Mittal, Aditya, et al.
Published: (2026)
by: Mittal, Aditya, et al.
Published: (2026)
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
by: Wu, Muling, et al.
Published: (2024)
by: Wu, Muling, et al.
Published: (2024)
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation
by: Shen, Junhong, et al.
Published: (2024)
by: Shen, Junhong, et al.
Published: (2024)
The Impact of Element Ordering on LM Agent Performance
by: Chi, Wayne, et al.
Published: (2024)
by: Chi, Wayne, et al.
Published: (2024)
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
by: Bai, Yuelin, et al.
Published: (2024)
by: Bai, Yuelin, et al.
Published: (2024)
Normalization through Fine-tuning: Understanding Wav2vec 2.0 Embeddings for Phonetic Analysis
by: Wang, Yiming, et al.
Published: (2025)
by: Wang, Yiming, et al.
Published: (2025)
AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference
by: Han, Yang, et al.
Published: (2024)
by: Han, Yang, et al.
Published: (2024)
Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning
by: Huang, Chenxi, et al.
Published: (2025)
by: Huang, Chenxi, et al.
Published: (2025)
Learning or Self-aligning? Rethinking Instruction Fine-tuning
by: Ren, Mengjie, et al.
Published: (2024)
by: Ren, Mengjie, et al.
Published: (2024)
Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
by: Zhou, Huichi, et al.
Published: (2025)
by: Zhou, Huichi, et al.
Published: (2025)
PoLi-RL: A Point-to-List Reinforcement Learning Framework for Conditional Semantic Textual Similarity
by: Song, Zixin, et al.
Published: (2025)
by: Song, Zixin, et al.
Published: (2025)
TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning
by: Wu, Jinyang, et al.
Published: (2025)
by: Wu, Jinyang, et al.
Published: (2025)
Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!
by: Zhang, Zhexin, et al.
Published: (2025)
by: Zhang, Zhexin, et al.
Published: (2025)
Information Guided Regularization for Fine-tuning Language Models
by: Sharma, Mandar, et al.
Published: (2024)
by: Sharma, Mandar, et al.
Published: (2024)
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs
by: Zhou, Runlong, et al.
Published: (2024)
by: Zhou, Runlong, et al.
Published: (2024)
ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
by: Du, Yiming, et al.
Published: (2025)
by: Du, Yiming, et al.
Published: (2025)
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances
by: Khodak, Mikhail, et al.
Published: (2023)
by: Khodak, Mikhail, et al.
Published: (2023)
Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models
by: Wang, Yu, et al.
Published: (2025)
by: Wang, Yu, et al.
Published: (2025)
Mechanistic Fine-tuning for In-context Learning
by: Cho, Hakaze, et al.
Published: (2025)
by: Cho, Hakaze, et al.
Published: (2025)
In-Place Test-Time Training
by: Feng, Guhao, et al.
Published: (2026)
by: Feng, Guhao, et al.
Published: (2026)
Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows
by: Chen, Valerie, et al.
Published: (2025)
by: Chen, Valerie, et al.
Published: (2025)
Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption
by: Yu, Rebecca, et al.
Published: (2025)
by: Yu, Rebecca, et al.
Published: (2025)
Agreement-Based Cascading for Efficient Inference
by: Kolawole, Steven, et al.
Published: (2024)
by: Kolawole, Steven, et al.
Published: (2024)
Data-efficient LLM Fine-tuning for Code Generation
by: Lv, Weijie, et al.
Published: (2025)
by: Lv, Weijie, et al.
Published: (2025)
Preference-grounded Token-level Guidance for Language Model Fine-tuning
by: Yang, Shentao, et al.
Published: (2023)
by: Yang, Shentao, et al.
Published: (2023)
Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes
by: Setlur, Amrith, et al.
Published: (2026)
by: Setlur, Amrith, et al.
Published: (2026)
Scaling Latent Reasoning via Looped Language Models
by: Zhu, Rui-Jie, et al.
Published: (2025)
by: Zhu, Rui-Jie, et al.
Published: (2025)
Similar Items
-
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
by: Li, Shanda, et al.
Published: (2025) -
CoMind: Towards Community-Driven Agents for Machine Learning Engineering
by: Li, Sijie, et al.
Published: (2025) -
FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization
by: Feng, Shengyu, et al.
Published: (2025) -
Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
by: Wu, Yangzhen, et al.
Published: (2024) -
Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection
by: Li, Sijie, et al.
Published: (2026)