1 citations · 2 across the 10 of their papers we have counts for
11 papers
Thinking Leakage: A Causal Audit of NoThink Post-Training in Hybrid Reasoning Models
Zehao Liu, Vasant G. Honavar
Post-training hybrid reasoning models in NoThink mode has attracted growing interest as a way to improve performance while keeping inference fast. However, these gains may draw on…
Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection
Debarshi Kundu, Swaroop Ghosh, Vasant Honavar
Data-adaptive sparse attention masks substantially outperform fixed patterns (e.g., BigBird and Longformer) and can even exceed dense attention on long sequences. Existing adaptive…
Restoring the Sweet Spot: Pass-Rate Weighted Self-Distillation for LLM Reasoning
Zehao Liu, Yuanpu Cao, Jinghui Chen +1
Self-Distillation Policy Optimization (SDPO) provides dense token-level credit assignment for reinforcement learning with large language models by leveraging the model's own feedba…
Projection-Free Transformers via Gaussian Kernel Attention
Debarshi Kundu, Archisman Ghosh, Swaroop Ghosh +1
Self-attention in Transformers is typically implemented as , where , , and are learned linear projections of the input…
Hyperdimensional Cross-Modal Alignment of Frozen Language and Image Models for Efficient Image Captioning
Abhishek Dalvi, Vasant Honavar
Large unimodal foundation models for vision and language encode rich semantic structures, yet aligning them typically requires computationally intensive multimodal fine-tuning. Suc…
Simple Denoising Diffusion Language Models
Huaisheng Zhu, Zhengyu Chen, Shijie Zhou +8
Recent Uniform State Diffusion Models (USDMs), initialized from a uniform prior, offer the promise of fast text generation due to their inherent self-correction ability compared to…
On a Connection Between Imitation Learning and RLHF
Teng Xiao, Yige Yuan, Mingxiao Li +2
This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcem…
SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters
Teng Xiao, Yige Yuan, Zhengyu Chen +4
Existing preference optimization objectives for language model alignment require additional hyperparameters that must be extensively tuned to achieve optimal performance, increasin…
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
Teng Xiao, Yige Yuan, Huaisheng Zhu +2
We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with avai…
GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules
Teng Xiao, Chao Cui, Huaisheng Zhu +1
Pretraining molecular representations is crucial for drug and material discovery. Recent methods focus on learning representations from geometric structures, effectively capturing…
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective
Teng Xiao, Mingxiao Li, Yige Yuan +3
This paper introduces a novel generalized self-imitation learning () framework, which effectively and efficiently aligns large language models with offline demonstra…