2 citations · 6 across the 25 of their papers we have counts for
5 papers · 1 filter
DeepLoop: Depth Scaling for Looped Transformers
Shuzhen Li, Yifan Zhang, Jiacheng Guo +2
Looped Transformers scale sequential computation by applying a compact stack of physical blocks for multiple rounds, increasing unrolled depth without increasing stored parameters.…
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
Kaixuan Huang, Jiacheng Guo, Zihao Li +15
Large language models have demonstrated impressive performance on challenging mathematical reasoning tasks, which has triggered the discussion of whether the performance is achieve…
Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs
Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda +10
This study introduces bifurcated attention, a method designed to enhance language model inference in shared-context batch decoding scenarios. Our approach addresses the challenge o…
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
Jiacheng Guo, Minshuo Chen, Huan Wang +3
This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be…
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
Jiacheng Guo, Zihao Li, Huazheng Wang +3
In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dim…