3 citations · 15 across the 55 of their papers we have counts for
6 papers · 1 filter
Understanding the Synergy between SFT, RLVR, and OPD in LLM Post-Training
Emre Can Acikgoz, Yang Li, Zeyu Leo Liu +4
Modern LLM post-training composes supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and on-policy distillation (OPD) into multi-stage pipelines,…
Learning Generalizable Behaviors for Terminal Agents
Yihang Yao, Bo Pang, Xuan Phi Nguyen +3
Terminal agents are a compelling application of large language models (LLMs), with the potential to integrate deeply into users' daily workflows. Reinforcement learning (RL) is a k…
The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering
Yefan Zhou, Yilun Zhou, Austin Xu +3
Generative verifiers have emerged as a promising paradigm for step-wise verification, but their verification behavior is often poorly calibrated: they may be under-critical and mis…
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…
Gradually Compacting Large Language Models for Reasoning Like a Boiling Frog
Yiran Zhao, Shengyang Zhou, Zijian Wu +7
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, but their substantial size often demands significant computational resources. To reduce resource c…
Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts
Xuan-Phi Nguyen, Shrey Pandit, Austin Xu +2
Mixture-of-Experts (MoE) models are typically pre-trained with explicit load-balancing constraints to ensure statistically balanced expert routing. Despite this, we observe that ev…