1 citations · 1 across the 9 of their papers we have counts for
10 papers
Tracking the Moving Frontier: Long-Short Term Advantage Estimator
Xinhao Yao, Lu Yu, Changhao Wang +5
Group-based RLVR methods estimate advantages by repeatedly sampling multiple trajectories for each prompt, making long-horizon agent training expensive and discarding useful experi…
Improving Cross-Format Robustness in Language Models with Multi-Format Training
June M. Liu, Shaomian Zheng, He Cao +3
Large language models often remain sensitive to answer format: a question solved correctly in one form may fail in another semantically equivalent form. To study this gap, we defin…
Measuring language complexity from hierarchical reuse of recurring patterns
Junyi Zhou, Rui Liu, Pengyu Liu +1
We introduce the ladderpath index as a measure of language complexity grounded in algorithmic information theory. It counts the minimum steps needed to reconstruct a sequence throu…
RubricBench: Aligning Model-Generated Rubrics with Human Standards
Qiyuan Zhang, Junyi Zhou, Yufei Wang +8
As Large Language Model (LLM) alignment evolves from simple completions to complex, highly sophisticated generation, Reward Models are increasingly shifting toward rubric-guided ev…
LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model
Zebin You, Xiaolu Zhang, Jun Zhou +2
We present \textbf{LLaDA-o}, an effective and length-adaptive omni diffusion model for multimodal understanding and generation. LLaDA-o is built on a Mixture of Diffusion (MoD) fra…
GRIP: Geometric Refinement and Adaptive Information Potential for Data Efficiency
Changhao Wang, Jiaolong Yang, Xinhao Yao +7
The performance of Large Language Models (LLMs) is increasingly governed by data efficiency rather than raw scaling volume. However, existing selection methods often decouple globa…
Improving Autoformalization Using Direct Dependency Retrieval
Shaoqi Wang, Lu Yu, Siwei Lou +4
The convergence of deep learning and formal mathematics has spurred research in formal verification. Statement autoformalization, a crucial first step in this process, aims to tran…
Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation
Ling Team, Ang Li, Ben Liu +138
We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of bi…
The Debate on RLVR Reasoning Capability Boundary: Shrinkage, Expansion, or Both? A Two-Stage Dynamic View
Xinhao Yao, Lu Yu, Xiaolin Hu +4
The ongoing debate on whether reinforcement learning with verifiable rewards (RLVR) expands or shrinks the reasoning capabilities of large language models (LLMs) remains unresolved…
Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
Ling Team, Binwei Zeng, Chao Huang +71
In this technical report, we tackle the challenges of training large-scale Mixture of Experts (MoE) models, focusing on overcoming cost inefficiency and resource limitations preval…