112 citations · 144 across the 40 of their papers we have counts for
9 papers · 1 filter
SEED: Self-Speculative Decoding via Implicit Encoder-Decoder
Hankun Lin, Patrick Pynadath, Ruqi Zhang
Self-speculative decoding accelerates large language model (LLM) inference by drafting tokens from the target model itself, but faces a sharp tradeoff between the quality and cost…
Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement
Yi Ding, Ruqi Zhang
On-policy distillation (OPD) offers dense token-level supervision as an alternative to the sparse outcome-level advantages of reinforcement learning with verifiable rewards (RLVR).…
Gradient-Guided Reward Optimization for Inference-time Alignment
Hankun Lin, Ruqi Zhang
Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment methods such as Best-of- and…
Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging
Pinaki Mohanty, Ruqi Zhang
High-dimensional and complex discrete distributions often exhibit multimodal behavior due to inherent discontinuities, posing significant challenges for sampling. Gradient-based di…
DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification
Ziyi Wang, Siva Rajesh Kasa, Ankith M S +6
Speculative decoding is an effective technique for accelerating large language model inference by drafting multiple tokens in parallel. In practice, its speedup is often bottleneck…
Generative Frontiers: Why Evaluation Matters for Diffusion Language Models
Patrick Pynadath, Jiaxin Shi, Ruqi Zhang
Diffusion language models have seen exciting recent progress, offering far more flexibility in generative trajectories than autoregressive models. This flexibility has motivated a…
SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology
Yifan Wang, Bolian Li, David Cho +3
Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard to use in open-ended domains whe…
Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World
Joonkyung Kim, Wenxi Chen, Davood Soleymanzadeh +9
The integration of foundation models (FMs) into robotics has accelerated real-world deployment, while introducing new safety challenges arising from open-ended semantic reasoning a…
Learning Self-Correction in Vision-Language Models via Rollout Augmentation
Yi Ding, Ziliang Qiu, Bolian Li +1
Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning (RL) methods struggle to learn it, as…