SpaRLax: sparse RL relaxation improves soft Chain-of-Thought reasoning
The arXiv paper (2610.27220v1) proposes SpaRLax, a sparse relaxation method for reinforcement-learning-style training of soft Chain-of-Thought (soft‑CoT). SpaRLax uses entmax to adaptively control sparsity so gradients can propagate across soft tokens over time; the method reportedly achieves the highest average results versus baselines across nine reasoning benchmarks and shows that adaptive sparsity and soft-token superposition contribute to gains.