Portrait de Yinhan Lu

Yinhan Lu

yinhan.lu@mila.quebec
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Bang Liu
Co-supervisor
David Ifeoluwa Adelani
Sujets de recherche
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners
Haochen Shi
Tianwen Fu
Qianggang Ding
Yinhan Lu
Yizhan Li
Huan Zhang
Yajie Zhao
Xingdi Yuan
Hengyang Lu
Bang Liu
People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason… (voir plus) by repeatedly applying a learned update rule to latent states, can also scale inference through more updates (depth) or additional trajectories generated by perturbing latent states (width). When people hit a dead end in their thinking, they may step back and approach the problem from a fresh angle. However, neither depth scaling nor perturbation-based width scaling of recursive reasoners can reliably achieve this: both may converge to incorrect attractors. To address this, we propose to explore diverse reasoning traces by applying different update maps to the same latent state, without any additional training. We realize this idea through \emph{Input-Conditioned Search (ICS)}, which searches over input conditions to induce alternative update maps from the pretrained reasoner. Across six reasoning tasks, ICS achieves the highest mean accuracy, while requiring up to two orders of magnitude fewer model FLOPs than the evaluated scaling baselines. Controlled experiments confirm that ICS can redirect trajectories from the same state. Our theoretical analysis and rigorous numerical verification also confirm that ICS can locally eliminate incorrect attracting fixed points. Together, these findings establish ICS as an effective axis for efficient test-time scaling and motivate further research on shaping recursive reasoning during training and at test time.
2026-10-12
arXiv (prépublication)
doi.org
arxiv.org
Simplifying the Modeling of Arbitrary Conditionals in Natural Language
Yinhan Lu
Eric Elmoznino
Leo Gagnon
Sarthak Mittal
Tejas Kasetty
Guillaume Lajoie
Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right… (voir plus) decoding and conditional likelihood computation. However, they cannot tractably sample from or evaluate arbitrary conditionals -- e.g., a block of text conditioned on past and future tokens. Recent work aims to solve this problem through novel architectures, but they often lead to sub-optimal modeling of such conditionals and degraded generations. We propose Arbitrary Conditionals GPT (AC-GPT) which introduces a simple modification to standard causal Transformers to enable evaluating and sampling from arbitrary conditionals -- including past, future, and mixed contexts -- within a single forward pass. Unlike prior approaches, our method preserves the standard left-to-right ordering and next-token prediction objective essential for both strong performance and efficient training on natural language. Crucially, this compatibility allows existing LLMs to be fine-tuned for arbitrary conditioning. Our empirical results indicate that our method outperforms baselines on modeling arbitrary conditionals, without degrading standard left-to-right performance.
2026-06-11
arXiv (prépublication)
doi.org
arxiv.org