Experiments on mathematical reasoning with Qwen3 and Qwen3.5 backbones show that Self-Routing consistently improves over uniform GRPO, uniform OPSD, fixed mixtures, and simpler routing baselines. ^[1] The paper's authors propose Self-Routing, a behavior-conditioned post-training framework for large language models that uses rollout correctness and confidence to decide how each sample should be optimized. ^[2] On LLaMA-3.1 (8B and 70B) and Qwen3 (0.6B-32B) at W4A16, the REAL-Q method reduces end-to-end KL divergence by up to approximately 49% relative to state-of-the-art globally-guided methods, as claimed by the paper's authors. ^[3] The paper 'REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent', authored by Qian Zhang, Yaoming Li, and co-authors, proposes REAL-Q, a post-training quantization paradigm for large language models that targets an end-to-end-aligned surrogate of the global loss and refines it via dynamic block-wise gradient descent applied after every column block of 128 columns. ^[4] The author spent several months building a custom 2-bit quantization scheme for the Qwen3 model. ^[5] The throughput of the 2-bit quantized Qwen3 model was barely faster than the FP16 baseline. ^[6]