Meta 与卡内基梅隆论文：RL 训练的 Qwen3-8B 工具选择超越提示词驱动的 Llama-4-Maverick 402B

Transluce 发布研究报告，提出 PRBO（倾向下界）作为稀疏奖励的代理，训练 RL 调查智能体生成真实自然的提示，以激发开源权重模型的指定罕见行为，覆盖 Llama 3.1/4、Qwen 2.5 和 DeepSeek-V3。

Many recent open-weight language models use n-gram embedding tables to improve performance. These tables store learned representations of short token sequences, such as pairs or triples of consecutive tokens, which the model retrieves duri…

Multi-token prediction (MTP) can accelerate language-model decoding by drafting several tokens and verifying them in a single target-model pass. However, the best speculation horizon changes with context: longer drafts offer greater amorti…

As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern. While Autoregressive (AR) models are the industry standard, Diffusion Language Models (DLLMs) have emerged as a pro…

LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their outputs. To bridge this gap, Knowledge Extraction Enhancement (KEE) techniques such as a…

Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and dense models. We study novel connections between downstream performance of compressed mo…

清华 NLP（OpenBMB 成员）联合中科院大学、东北大学、UIUC 和约翰霍普金斯大学提出 One-Shot OPD，将 OPD 训练集压缩到一条查询：数学任务上从 59.1 提升至 68.5（300 步），达到全量数据 OPD 69.8 的 87% 增益，并在代码、指令跟随和智能体工具使用上跨 Qwen、Llama、OLMo 成立。

Sliding-window KV inference refers to processing a sequence incrementally while retaining only a fixed-size cache of recent key and value states. It can be applied to pretrained causal transformers at inference time without additional trai…

研究在 Llama3 与 Qwen2.5 系列上做强弱蒸馏实验，独立改变 rollout 策略、token 级 KL 方向和学习率，发现 rollout 策略并非核心因素：前向 KL 对 rollout 策略极不敏感且表现稳定，反向 KL 则明显更敏感、偏好学生模型自生成的 rollout，而学习率主导遗忘与更新稀疏性。

针对多教师在线策略蒸馏（MOPD）中的能力跷跷板问题，研究者提出 PMOPD，通过从各任务累积参数位移构建子空间记忆，对梯度和优化器更新做投影以消除跨任务干扰，并配合轻量冲突探针指导任务排序与循环策略。在 Code、Reason、Math 任务上，PMOPD 全面优于 MOPD，Qwen2.5-7B 平均分提升 2.54 分，Llama-3.1-8B 提升 2.09 分。