3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning
Seil Kang, Hangoo Kang, Tarun Suresh +4
Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these agents requires long, heterogeneous trajectorie…
Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training
Tarun Suresh, Pranshu Chaturvedi, Hangoo Kang +4
Block diffusion language models (BDLMs) combine autoregressive dependencies across blocks with parallel denoising within blocks, but long-context training is constrained by distrib…
Training Hybrid Block Diffusion Language Models with Partial Bidirectionality
Pranshu Chaturvedi, Parth Shroff, Tarun Suresh +2
High-throughput long-context generation is one of the central challenges for large language models. Generation is typically memory-bandwidth-bound rather than compute-bound: each d…
TRACE: Capability-Targeted Agentic Training
Hangoo Kang, Tarun Suresh, Jon Saad-Falcon +1
Models often fail to complete agentic tasks because they lack core capabilities required by the target environment. However, mainstream approaches for addressing these failures typ…