243 citations · 461 across the 25 of their papers we have counts for
8 papers · 1 filter
Learning from Language Feedback via Variational Policy Distillation
Yang Li, Erik Nijkamp, Semih Yavuz +1
Reinforcement learning from verifiable rewards (RLVR) suffers from sparse outcome signals, creating severe exploration bottlenecks on complex reasoning tasks. Recent on-policy self…
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong +2
Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representat…
Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
Omead Pooladzandi, Pasha Khosravi, Erik Nijkamp +1
Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality. In this paper, we propose using…
BigIssue: A Realistic Bug Localization Benchmark
Paul Kassianik, Erik Nijkamp, Bo Pang +2
As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language…
ProGen2: Exploring the Boundaries of Protein Language Models
Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein +2
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein…
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi +5
Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The…
Generative Text Modeling through Short Run Inference
Bo Pang, Erik Nijkamp, Tian Han +1
Latent variable models for text, when trained successfully, accurately model the data distribution and capture global semantic and syntactic features of sentences. The prominent ap…
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling
Bo Pang, Erik Nijkamp, Jiali Cui +2
This paper proposes a latent space energy-based prior model for semi-supervised learning. The model stands on a generator network that maps a latent vector to the observed example.…