3 papers
cs.LG2026
X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths
Bowen Dong, Yilong Fan, Tengyu Pan +6
Mixture-of-Depths (MoD) enables conditional computation across Transformer depth by routing only a subset of tokens through selected layers, but its original one-sparse--one-dense…
cs.LG2026
Gradient Starvation in Binary-Reward GRPO: Why Group-Mean Centering Fails and Why the Simplest Fix Works
Wenhua Nie, Jianan Wu, Junlin Liu +6
Group Relative Policy Optimization (GRPO) is a standard algorithm for reinforcement learning from verifiable rewards, but its group-mean-centered advantage can fail under binary re…
cs.LG2026
The Coupling Tax: How Shared Token Budgets Undermine Visible Chain-of-Thought Under Fixed Output Limits
Wenhua Nie, Junlin Liu, Jianan Wu +5
Chain-of-thought reasoning is often treated as a monotone way to improve language-model accuracy by letting a model think longer. We identify a countervailing effect, the coupling…