Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for simple problems. We present Length-Adaptive Policy Optimization (LAPO), a novel framework that transforms reasoning length control from an external constraint into an intrinsic model capability. Unlike existing approaches that impose rigid limits or rely on post-hoc interventions, LAPO enables models to internalize an understanding of appropriate reasoning depth through a two-stage reinforcement learning process. In the first stage, models learn natural reasoning patterns by discovering the statistical distribution of successful solution lengths. In the second stage, these learned patterns are embedded as in-context, self-declarative guidance, teaching the model to proactively plan its reasoning budget. Experiments on mathematical reasoning benchmarks demonstrate that LAPO reduces token usage by up to 38.5% while improving accuracy by 2.3%. Our analysis reveals that models trained with LAPO develop emergent abilities to allocate computational resources based on problem complexity, achieving efficient reasoning without sacrificing quality.

Theory of Mind (ToM), the ability to infer others’ mental states from behavior, is pivotal for developing machines with human-level social intelligence. Existing methods endowing LLMs with ToM fall into two paradigms: training-free methods and those repurposing ToM evaluation benchmarks as training data for RL-based fine-tuning. However, training-free methods fail to internalize the augmented ToM into the LLMs. Meanwhile, using evaluation benchmarks as training sources is conceptually problematic and, in practice, results in narrow in-domain overfitting rather than robust ToM. To address the lack of training resources within the ToM community and to empower LLMs with robust ToM, we introduce ToM-Synth, a factorial combinatorial synthesis framework of 6912 social units. This framework enables the systematic synthesis of ToM data, yielding a training dataset of 27,648 instances, termed ToM-Synth-27K. Utilizing ToM-Synth-27K for RL fine-tuning, experimental results demonstrate consistent and significant improvements across models of varying families and scales on ToM, Emotional Intelligence, and Social Commonsense benchmarks. Furthermore, we observe concurrent enhancements in IQ-related tasks (math, science, logic) and effective performance scaling with increasing data scale.

Data-analytic agents must iteratively generate and execute code while interpreting real-time feedback to refine their analytical strategies. Existing training paradigms decouple the agent’s policy from the underlying environment dynamics, producing agents that learn to recover from errors rather than to avoid them: failure rates rise alongside recovery rates, and interaction turns inflate as agents resort to trial-and-error exploration. We introduce DataSight, a framework that internalizes environment dynamics by making the model’s anticipation of execution outcomes an explicit, structured output accompanying every generated action. DataSight first enriches expert trajectories with foresight prediction fields grounded in real execution feedback, then applies a dual-stage RL curriculum that calibrates prediction fidelity before optimizing long-horizon task performance, and at inference time intercepts anticipated failures before physical execution. Trained on only 7,273 trajectories, DataSight-4B surpasses both DataMind-7B and DeepAnalyse-8B in average accuracy, while DataSight-8B achieves 64.13% Avg@3 across four benchmarks with Pass@3 of 74.81%, approaching DeepSeek-V3.2 at 75.56%. Analysis confirms that foresight training generalizes across non-reasoning backbones and SQL-based tasks, improving both single-step code quality and multi-turn interaction efficiency.