Speech codecs provide an important interface between continuous speech signals and large language models. An ideal codec for speech language models should not only preserve acoustic information but also capture rich semantic information. However, existing codecs struggle to balance these objectives at low bitrates. We propose XY-Tokenizer, a low-bitrate speech codec (around 1 kbps) trained with a structured multi-stage, multi-task strategy that aligns discrete speech representations with text while preserving fine-grained acoustic details for reconstruction. This design explicitly mitigates the semantic–acoustic conflict observed in prior low-bitrate codecs. Experiments show that XY-Tokenizer achieves stronger semantic alignment than representative semantic-distillation codecs such as SpeechTokenizer and Mimi, while maintaining high-quality speech reconstruction across both clean and out-of-distribution conditions. Furthermore, XY-Tokenizer consistently outperforms existing low-bitrate codecs in LLM-based speech understanding and generation tasks, demonstrating its effectiveness as a general-purpose speech representation for speech–language modeling.

Kullback-Leibler (KL) divergence regularization is essential for stabilizing reinforcement learning from human feedback (RLHF) in large language models (LLMs), yet its exact computation requires summing over vocabularies of all tokens, incurring prohibitive memory costs during training. Existing stochastic estimators circumvent this bottleneck by estimating KL divergence using only the sampled token from the trajectory, but suffer from high variance (k₁) or systematic bias (k₂). We propose TIKE (Top-k Importance-weighted KL Estimator), which exploits the Zipfian structure of language model distributions: by deterministically integrating over only the top-k tokens, TIKE captures most of the probability mass while effectively reducing memory cost. To ensure correctness in off-policy settings characteristic of Group Relative Policy Optimization (GRPO), we incorporate importance sampling weights that correct for distribution shift between rollout and optimization policies. Experiments on models across diverse benchmarks demonstrate that TIKE consistently outperforms stochastic baselines, while exhibiting substantially lower gradient variance. Our analysis reveals that TIKE closely tracks the exact Rao-Blackwellized estimator with near-zero variance, offering a practical path toward stable, memory-efficient KL regularization for reasoning-intensive LLMs training.