6 papers
Controlling Exploration-Exploitation in GFlowNets via Markov Chain Perspectives
Lin Chen, Samuel Drapeau, Fanghao Shao +5
Generative Flow Network (GFlowNet) objectives implicitly fix an equal mixing of forward and backward policies, potentially constraining the exploration-exploitation trade-off durin…
Towards Compressive and Scalable Recurrent Memory
Yunchong Song, Jushi Kai, Liming Lu +2
Transformers face a quadratic bottleneck in attention when scaling to long contexts. Recent approaches introduce recurrent memory to extend context beyond the current window, yet t…
Flow of Spans: Generalizing Language Models to Dynamic Span-Vocabulary via GFlowNets
Bo Xue, Yunchong Song, Fanghao Shao +5
Standard autoregressive language models generate text token-by-token from a fixed vocabulary, inducing a tree-structured state space when viewing token sampling as an action, which…
Next Concept Prediction in Discrete Latent Space Leads to Stronger Language Models
Yuliang Liu, Yunchong Song, Yixuan Wang +6
We propose Next Concept Prediction (NCP), a generative pretraining paradigm built on top of Next Token Prediction (NTP). NCP predicts discrete concepts that span multiple tokens, t…
FlowRL: Matching Reward Distributions for LLM Reasoning
Xuekai Zhu, Daixuan Cheng, Dinghuai Zhang +20
We propose FlowRL: matching the full reward distribution via flow balancing instead of maximizing rewards in large language model (LLM) reinforcement learning (RL). Recent advanced…
Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
Siyuan Huang, Yunchong Song, Jiayue Zhou +1
In the realm of graph learning, there is a category of methods that conceptualize graphs as hierarchical structures, utilizing node clustering to capture broader structural informa…