Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key–value states into a low-rank latent vector cKV, caching only this vector to reduce memory. In tensor parallelism (TP), however, attention heads are computed across multiple devices, and each device must load the full cKV, eroding the advantage of MLA over Grouped Query Attention (GQA). We present TPLA, a scheme that partitions both the latent representation and each head's input dimension across devices, performs attention independently on each shard, and aggregates the results with an all-reduce. Unlike GLA, every attention head in TPLA still attends to the full latent space, preserving MLA's representational capacity while reducing the per-device KV cache. To make TPLA drop-in compatible with MLA checkpoints, we further derive orthogonal reparameterizations of RMSNorm and softmax---instantiated with Hadamard and PCA transforms---that mitigate cross-shard discrepancies when slicing latent vectors across devices. Finally, we introduce a prefill-decode separation scheme that keeps the MLA form during compute-bound prefilling and switches to TPLA during memory-bound decoding, minimizing conversion-induced error. By reducing the per-device KV cache for DeepSeek-V3 and Kimi-K2, we achieve 1.79x) and 1.93) speedups respectively, at a 32K-token context length while maintaining accuracy on commonsense and LongBench benchmarks. TPLA can be further implemented on top of FlashAttention-3, enabling practical end-to-end acceleration.

Despite the impressive performance in a variety of complex tasks, modern large language models (LLMs) still have trouble dealing with some math problems that are simple and intuitive for humans, such as addition. While we can easily learn basic *rules* of addition and apply them to new problems of any length, LLMs struggle to do the same. Instead, they may rely on similar *cases* seen in the training corpus for help. We define these two different reasoning mechanisms as "*rule-based reasoning*" and "*case-based reasoning*". Since rule-based reasoning is essential for acquiring systematic generalization ability, we aim to explore exactly whether transformers use rule-based or case-based reasoning for math problems. Through carefully designed intervention experiments on five math tasks, we confirm that transformers are performing case-based reasoning, no matter whether scratchpad is used, which aligns with the previous observations that transformers use subgraph matching/shortcut learning to reason. To mitigate such problems, we propose a Rule-Following Fine-Tuning (RFFT) technique to teach transformers to perform rule-based reasoning. Specifically, we provide explicit rules in the input and then instruct transformers to recite and follow the rules step by step. Through RFFT, we successfully enable LLMs fine-tuned on 1-5 digit addition to generalize to up to 12-digit addition with over 95% accuracy, which is over 40% higher than scratchpad. The significant improvement demonstrates that teaching LLMs to use rules explicitly helps them learn rule-based reasoning and generalize better in length. Code is available at https://github.com/GraphPKU/Case_or_Rule.

Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment andperforming reasoning with the acquired knowledge—situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles. In Mars, agents need to actively interact with their surroundings, derive useful rules and perform decision-making tasks in specific contexts. We conduct experiments on various RL-based and LLM-based methods, finding that they all struggle on this challenging situated inductive reasoning benchmark. Furthermore, we explore Induction from Reflection, where we instruct agents to perform inductive reasoning from history trajectory. The superior performance underscores the importance of inductive reasoning in Mars. Through Mars, we aim to galvanize advancements in situated inductive reasoning and set the stage for developing the next generation of AI systems that can reason in an adaptive and context-sensitive way.