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] Researchers introduced IdeaForecastBench to evaluate whether large language models can anticipate subsequent research work based on existing literature. ^[2] 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. ^[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]