Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs. However, it is hindered by two critical challenges: (1) substantial GPU memory consumption to load all experts; (2) low activated parameters cannot be equivalently translated into inference acceleration effects. In this work, we propose EAC-MoE, an Expert-Selection Aware Compressor for MoE-LLMs, which deeply aligns with the characteristics of MoE from the perspectives of quantization and pruning, and introduces two modules to address these two challenges respectively: (1) The expert selection bias caused by low-bit quantization is a major factor contributing to the performance degradation in MoE-LLMs. Based on this, we propose Quantization with Expert-Selection Calibration (QESC), which mitigates the expert selection bias by calibrating the routers within the MoE; (2) There are always certain experts that are not crucial for the corresponding tasks, yet causing inference latency. Therefore, we propose Pruning based on Expert-Selection Frequency (PESF), which significantly improves inference speed by pruning less frequently used experts for current task. Extensive experiments demonstrate that our approach significantly reduces memory usage and improves inference speed with minimal performance degradation.

Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs’ deep thinking abilities generally require large-scale data or significant training efforts. Meanwhile, it remains unclear how to improve the thinking abilities of less powerful base models. In this work, we introduce S²R, an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference. Specifically, we first initialize LLMs with iterative self-verification and self-correction behaviors through supervised fine-tuning on carefully curated data. The self-verification and self-correction skills are then further strengthened by outcome-level and process-level reinforcement learning with minimized resource requirements. Our results demonstrate that, with only 3.1k behavior initialization samples, Qwen2.5-math-7B achieves an accuracy improvement from 51.0% to 81.6%, outperforming models trained on an equivalent amount of long-CoT distilled data. We also discuss the effect of different RL strategies on enhancing LLMs’ deep reasoning. Extensive experiments and analysis based on three base models across both in-domain and out-of-domain benchmarks validate the effectiveness of S²R.

State Space Models (SSMs), such as Mamba, have recently demonstrated potential in language understanding tasks, positioning them as competitors to transformer architectures. However, our investigations reveal that the Mamba architecture still has room for further optimization—not only in linear projections but also in state caches, which contribute significantly to memory consumption, particularly after quantizing the former into low bits. After a theoretical analysis of the causes of outliers in states, we propose Decoupled Scale Quantization (DSQ), which mitigates outliers in both the state and channel dimensions by applying separate quantization scales. To preserve the selective ability of quantized Mamba, we introduce Efficient Selectivity Reconstruction (ESR), a novel quantization simulation scheme in block-wise reconstruction that enables fast parallel scan algorithms with the non-linear quantization function. We demonstrate the effectiveness of Q-Mamba across various quantization settings, model sizes, and both generation and zero-shot tasks. In particular, for Mamba2-2.7B with W8A8H4 (8-bit weights and activations, 4-bit state caches) quantization, Q-Mamba achieves a 50% reduction in memory consumption with only a 2.13% average accuracy degradation on zero-shot tasks.

Delta compression methods focus on efficiently serving multiple uniquely fine-tuned models, each tailored to specific tasks and user requirements. These approaches decompose a fine-tuned LLM into a base model and corresponding delta weights, which are compressed using low-rank or low-bit representations to reduce storage costs. However, their effectiveness is highly sensitive to the magnitude of the model deltas—a factor directly influenced by the scale of the training data. We propose the Residual Quantization Tree (RQT), a hierarchical quantization framework that automatically shares low-bit integer weights across similar fine-tuned models. The RQT construction employs a two-phase greedy algorithm: a bottom-up aggregation of models based on weight matrix similarity, and top-down residual quantization, in which each node optimizes the quantization parameters and then delegates residual errors to child nodes. We evaluate RQT on fine-tuned models across mathematics, coding, chatbot, and Chinese LLMs. The results show that RQT achieves an average accuracy degradation of approximately 3% (comparable to previous 4-bit post-training quantization) while maintaining an effective bitwidth of around 2 bits.