CoScale-RL: Efficient Post-Training by Co-Scaling Data and Computation
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Yutong, Gao, Jiandong, Wu, Ji |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning
by: Chen, Yutong, et al.
Published: (2025)
by: Chen, Yutong, et al.
Published: (2025)
Dynamic feature selection in medical predictive monitoring by reinforcement learning
by: Chen, Yutong, et al.
Published: (2024)
by: Chen, Yutong, et al.
Published: (2024)
Learning-Zone Energy: Online Data Selection for Efficient RL Post-Training
by: Cui, Peng, et al.
Published: (2026)
by: Cui, Peng, et al.
Published: (2026)
AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training
by: Han, Zhenyu, et al.
Published: (2025)
by: Han, Zhenyu, et al.
Published: (2025)
Shorter Thoughts, Same Answers: Difficulty-Scaled Segment-Wise RL for CoT Compression
by: Tian, Ye, et al.
Published: (2026)
by: Tian, Ye, et al.
Published: (2026)
DiLoCoX: A Low-Communication Large-Scale Training Framework for Decentralized Cluster
by: Qi, Ji, et al.
Published: (2025)
by: Qi, Ji, et al.
Published: (2025)
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
by: Agrawalla, Bhavya, et al.
Published: (2025)
by: Agrawalla, Bhavya, et al.
Published: (2025)
CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
by: Yang, Zi, et al.
Published: (2024)
by: Yang, Zi, et al.
Published: (2024)
IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL
by: Cheng, Zhoujun, et al.
Published: (2026)
by: Cheng, Zhoujun, et al.
Published: (2026)
DiRL: An Efficient Post-Training Framework for Diffusion Language Models
by: Zhu, Ying, et al.
Published: (2025)
by: Zhu, Ying, et al.
Published: (2025)
Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models
by: Qu, Yun, et al.
Published: (2026)
by: Qu, Yun, et al.
Published: (2026)
Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings
by: Bean, Andrew M., et al.
Published: (2025)
by: Bean, Andrew M., et al.
Published: (2025)
How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment
by: Zhu, Rui, et al.
Published: (2026)
by: Zhu, Rui, et al.
Published: (2026)
Imbalanced Gradients in RL Post-Training of Multi-Task LLMs
by: Wu, Runzhe, et al.
Published: (2025)
by: Wu, Runzhe, et al.
Published: (2025)
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
by: Liu, Ziyue, et al.
Published: (2025)
by: Liu, Ziyue, et al.
Published: (2025)
Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms
by: Xiao, Jie, et al.
Published: (2025)
by: Xiao, Jie, et al.
Published: (2025)
RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?
by: Xu, Haotian, et al.
Published: (2025)
by: Xu, Haotian, et al.
Published: (2025)
A Dual-View Approach to Classifying Radiology Reports by Co-Training
by: Han, Yutong, et al.
Published: (2024)
by: Han, Yutong, et al.
Published: (2024)
RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm
by: Yang, Yongyi, et al.
Published: (2025)
by: Yang, Yongyi, et al.
Published: (2025)
LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training
by: Gwak, Minju, et al.
Published: (2026)
by: Gwak, Minju, et al.
Published: (2026)
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
by: Kim, Junhan, et al.
Published: (2024)
by: Kim, Junhan, et al.
Published: (2024)
A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
by: Sancaktar, Cansu, et al.
Published: (2026)
by: Sancaktar, Cansu, et al.
Published: (2026)
Scale Efficient Training for Large Datasets
by: Zhou, Qing, et al.
Published: (2025)
by: Zhou, Qing, et al.
Published: (2025)
RollArt: Scaling Agentic RL Training via Disaggregated Infrastructure
by: Gao, Wei, et al.
Published: (2025)
by: Gao, Wei, et al.
Published: (2025)
Scaling Offline RL via Efficient and Expressive Shortcut Models
by: Espinosa-Dice, Nicolas, et al.
Published: (2025)
by: Espinosa-Dice, Nicolas, et al.
Published: (2025)
Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning
by: Wu, Xiaojun, et al.
Published: (2025)
by: Wu, Xiaojun, et al.
Published: (2025)
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning
by: Tan, Zelin, et al.
Published: (2025)
by: Tan, Zelin, et al.
Published: (2025)
Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs
by: Chen, Zhengyu, et al.
Published: (2025)
by: Chen, Zhengyu, et al.
Published: (2025)
CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
by: Dai, Weinan, et al.
Published: (2026)
by: Dai, Weinan, et al.
Published: (2026)
ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking
by: Zhang, Qiang, et al.
Published: (2026)
by: Zhang, Qiang, et al.
Published: (2026)
Mapping Post-Training Forgetting in Language Models at Scale
by: Harmon, Jackson, et al.
Published: (2025)
by: Harmon, Jackson, et al.
Published: (2025)
AutoScale: Scale-Aware Data Mixing for Pre-Training LLMs
by: Kang, Feiyang, et al.
Published: (2024)
by: Kang, Feiyang, et al.
Published: (2024)
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
by: Li, Haozhan, et al.
Published: (2025)
by: Li, Haozhan, et al.
Published: (2025)
Mixtures of Experts Unlock Parameter Scaling for Deep RL
by: Obando-Ceron, Johan, et al.
Published: (2024)
by: Obando-Ceron, Johan, et al.
Published: (2024)
Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
by: Chen, Hao Mark, et al.
Published: (2025)
by: Chen, Hao Mark, et al.
Published: (2025)
Understanding the Role of Training Data in Test-Time Scaling
by: Javanmard, Adel, et al.
Published: (2025)
by: Javanmard, Adel, et al.
Published: (2025)
When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
by: Wu, Boqian, et al.
Published: (2026)
by: Wu, Boqian, et al.
Published: (2026)
$Q\sharp$: Provably Optimal Distributional RL for LLM Post-Training
by: Zhou, Jin Peng, et al.
Published: (2025)
by: Zhou, Jin Peng, et al.
Published: (2025)
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
by: Liu, Mingjie, et al.
Published: (2025)
by: Liu, Mingjie, et al.
Published: (2025)
P$^2$ Law: Scaling Law for Post-Training After Model Pruning
by: Chen, Xiaodong, et al.
Published: (2024)
by: Chen, Xiaodong, et al.
Published: (2024)
Similar Items
-
Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning
by: Chen, Yutong, et al.
Published: (2025) -
Dynamic feature selection in medical predictive monitoring by reinforcement learning
by: Chen, Yutong, et al.
Published: (2024) -
Learning-Zone Energy: Online Data Selection for Efficient RL Post-Training
by: Cui, Peng, et al.
Published: (2026) -
AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training
by: Han, Zhenyu, et al.
Published: (2025) -
Shorter Thoughts, Same Answers: Difficulty-Scaled Segment-Wise RL for CoT Compression
by: Tian, Ye, et al.
Published: (2026)