4 papers · 1 filter
GIFT: Gradient Informed Filtering for Distillation Augmented Reasoning RL
Shijie Li, Yilin Gao, Tieyuan Chen +3
Training large language models for complex reasoning tasks relies on two complementary signals: sparse, outcome-grounded reinforcement learning (RL) for correctness, and dense, tok…
LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation
Zefeng He, Yunkai Chai, Xiaoyang Chen +5
Looped Mixture-of-Experts (MoE) models combine parameter reuse with sparse computation to improve performance under matched budgets. Prior work demonstrates that different loop vis…
TPPO: Tracking-Value PPO For Language Model Reasoning
Futing Wang, Chenxi Li, Jianhao Yan +6
Proximal Policy Optimization (PPO) is widely used in reinforcement learning with verifiable rewards for language-model reasoning. While \emph{value evolution along trajectory} char…
TEMPO: Scaling Test-time Training for Large Reasoning Models
Qingyang Zhang, Xinke Kong, Haitao Wu +7
Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the reach of offline training. De…