14 papers
Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks
Yihua Zhu, Qianying Liu, Fei Cheng +4
Reinforcement learning with verifiable rewards (RLVR) has become central to post-training reasoning models, yet a key limitation of existing studies is their narrow view of the rea…
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs
Yihua Zhu, Qianying Liu, Jiaxin Wang +5
Autoregressive LLMs perform well on relational tasks that require linking entities via relational words (e.g., father/son, friend), but it is unclear whether they learn the logical…
Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings
Ryo Kishino, Yusuke Takase, Momose Oyama +2
Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterogeneous settings. We extend this…
Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors
Yusuke Takase, Momose Oyama, Hidetoshi Shimodaira
We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions. In…
Domain Mixture Design via Log-Likelihood Differences for Aligning Language Models with a Target Model
Ryo Kishino, Riku Shiomi, Hiroaki Yamagiwa +2
Instead of directly distilling a language model, this study addresses the problem of aligning a base model with a target model in distribution by designing the domain mixture of tr…
Measuring Affinity between Attention-Head Weight Subspaces via the Projection Kernel
Hiroaki Yamagiwa, Yusuke Takase, Hidetoshi Shimodaira
Understanding relationships between attention heads is essential for interpreting the internal structure of Transformers, yet existing metrics do not capture this structure well. W…