Reinforcement learning from verifiable rewards (RLVR) has become a promising paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing RL-based training remains only a rough approximation to human learning, where humans leverage external experience to guide exploration and gradually internalize problem-solving strategies into stable knowledge. Motivated by this gap, we ask: how can LLMs better utilize and internalize experience during RLVR training? To answer this question, we propose Dual Guidance Optimization (DGO), which treats experience as a bridge between external guidance and internal policy learning. Rather than using experience only as an inference-time aid or trajectories only as optimization signals, DGO couples them in a closed loop: experience guides exploration, improved trajectories renew the experience bank, and guided trajectories are rewritten as self-contained traces for policy distillation. This enables the model to better utilize external experience while yielding consistent gains under experience-free inference. Experiments across multiple benchmarks show that DGO consistently outperforms baselines under both experience-free and experience-guided inference, suggesting that coupling utilization and internalization leads to effective reasoning.

While large language models (LLMs) have mastered syntax-level code generation, complex algorithmic reasoning remains a challenge, typically addressed by scaling model depth and parameter count. Universal Transformers (UT) offer a compelling alternative by introducing a recurrent inductive bias that aligns with the recursive nature of programming logic. However, training looped architectures at scale has historically been hindered by severe instability and optimization difficulties associated with backpropagation through time (BPTT). We present LoopCoder (40B-A80B) pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants—the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters. Unlike prior approaches that restrict recurrence to small-scale tasks, we implement a comprehensive looped training protocol spanning both pre-training and post-training phases. We initiate the model via dense-to-loop transformation, folding a pre-trained dense checkpoint to initialize a recurrent block, followed by rigorous looped pre-training and specialized post-training for instruction following and reasoning. Our results establish a robust recipe for scaling coding intelligence via recurrent computation, proving that dense checkpoints serve as an optimal foundation for evolving into dynamic, looped reasoners.

While large language models show promise in medical applications, achieving expert-level clinical reasoning efficiently remains challenging due to the need for massive amounts of manually labeled data and large-scale models. To address this challenge, we propose Clinical-Oriented Reinforcement Learning (CORL), the first fully open-source, end-to-end reinforcement learning training pipeline in the clinical reasoning domain, incorporating a Reasoning-Oriented Data Strategy (RODS) based on topological synthesis, CoT cold-start, and two-stage reinforcement learning. Through CORL, we trained the Fleming-R1 series of models. Among them, Fleming-R1-7B significantly outperforms models of comparable size while approaching or even surpassing certain 32B and 72B models. Fleming-R1-32B achieves near-parity with GPT-4o and outperforms the strongest open-source alternatives up to 671B in MedXpertQA. This demonstrates that in clinical reasoning field, a meticulously designed training pipeline holds greater importance than scaling model size alone. Data and Models are available at https://github.com/UbiquantAI/Fleming-R1 and https://huggingface.co/collections/IQuestLab/fleming.

Large language models (LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming languages (PLs) have varying impacts during pre-training that significantly affect base model performance, leading to inaccurate performance prediction. Existing works focus on language-agnostic settings, neglecting the inherently multilingual nature of modern software development. Therefore, it is first necessary to investigate the scaling laws of different PLs, and then consider their mutual influences to arrive at the final multilingual scaling law. In this paper, we present the first systematic exploration of scaling laws for multilingual code pre-training, conducting over 1000+ experiments (Equivalent to 336,000+ H800 hours) across multiple PLs, model sizes (0.2B to 14B parameters), and dataset sizes (1T tokens). We establish scaling laws for code LLMs across multiple programming languages, showing that interpreted languages benefit more from increased scale than compiled ones. Multilingual pre-training provides synergistic benefits, especially between syntactically similar languages, with parallel pairing (concatenating code with translations) significantly enhancing cross-lingual abilities. We propose a proportion-dependent multilingual scaling law that optimally allocates training tokens by prioritizing high-utility languages (e.g., Python), balancing high-synergy pairs (e.g., JavaScript-TypeScript), and reducing allocation to fast-saturating languages (e.g., Rust), achieving superior performance across all languages compared to uniform distribution.

Agents based on large language models have recently shown strong potential on real-world software engineering (SWE) tasks that require long-horizon interaction with repository-scale codebases. However, most existing agents rely on append-only context maintenance or passively triggered compression heuristics, which often lead to context explosion, semantic drift, and degraded reasoning in long-running interactions. We propose Cat, a new context management paradigm that elevates context maintenance to a callable tool integrated into the decision-making process of agents. Cat formalizes a structured context workspace consisting of stable task semantics, condensed long-term memory, and high-fidelity short-term interactions, and enables agents to proactively compress historical trajectories into actionable summaries at appropriate milestones. To support context management for SWE-agents, we propose a trajectory-level supervision framework, CaT-Generator, based on an offline data construction pipeline that injects context-management actions into complete interaction trajectories. Using this framework, we train a context-aware model, SWE-Compressor. Experiments on SWE-Bench-Verified demonstrate that SWE-Compressor reaches a 57.6% solved rate and significantly outperforms ReAct-based agents and static compression baselines, while maintaining stable and scalable long-horizon reasoning under a bounded context budget.

The rapid advancement of code large language models (LLMs) has sparked significant research interest in systematically evaluating their code generation capabilities, yet existing benchmarks predominantly assess models at a single structural granularity and focus on limited programming languages, obscuring fine-grained capability variations across different code scopes and multilingual scenarios. We introduce M²G-Eval, a multi-granularity, multilingual framework for evaluating code generation in large language models (LLMs) across four levels: Class, Function, Block, and Line. Spanning 18 programming languages, M²G-Eval includes 17K+ training tasks and 1,286 human-annotated, contamination-controlled test instances. We develop M²G-Eval-Coder models by training Qwen3-8B with supervised fine-tuning and Group Relative Policy Optimization. Evaluating 30 models (28 state-of-the-art LLMs plus our two M²G-Eval-Coder variants) reveals three main findings: (1) an apparent difficulty hierarchy, with Line-level tasks easiest and Class-level most challenging; (2) widening performance gaps between full- and partial-granularity languages as task complexity increases; and (3) strong cross-language correlations, suggesting that models learn transferable programming concepts. M²G-Eval enables fine-grained diagnosis of code generation capabilities and highlights persistent challenges in synthesizing complex, long-form code.

Industrial code tasks, including chip design, GPU optimization, and firmware, often fail for reasons that are invisible in ordinary source code: timing constraints, backend logs, layout data, simulator behavior, and hardware-specific performance trade-offs. Current code models see many programs but few expert correction traces from these settings. We present InCoder-32B-Thinking, a 32B thinking code model trained with Error-driven Chain-of-Thought (ECoT) synthesis and an Industrial Code World Model (ICWM). ECoT converts several rounds of toolchain signals into reasoning traces by preserving the failed code, the observed failure, and the later fix. ICWM learns from execution traces in domains such as Verilog simulation and GPU profiling, then predicts feedback for the data engine, which can build more repair traces without routing every candidate through a real toolchain. The data keeps the uneven reasoning depth of industrial work: short traces for simple cases, long traces for hardware-level debugging. Across 23 tests (14 general and 9 industrial), InCoder-32B-Thinking reaches 81.3% on LiveCodeBench v5, 84.0% CAD-Coder compile success, and 38.0% on KernelBench L2, while remaining competitive on general coding tasks. Model weights and code are available at Hugging Face: https://huggingface.co/Multilingual-Multimodal-NLP/IndustrialCoder and GitHub: https://github.com/CSJianYang/Industrial-Coder.

Deep research and agent evolution serve as de-facto tasks for AI agents in real-world applications toward artificial general intelligence. The former enables autonomous retrieval and integration of information in open-ended environments to tackle open-ended research tasks, yet it is constrained by the static parametric deep research capabilities of agent systems. The latter allows agents to autonomously interact with the environment to gain experiences that evolve model capabilities. However, its effectiveness has been widely validated only on verifiable tasks with standard answers, leaving a gap with open-ended research tasks. To bridge these two critical tasks, we propose the Hybrid Open-Ended Tri-Evolution (HOTE) framework, which leverages hybrid-mode reinforcement learning to facilitate the collaborative evolution of a proposer, solver and judge based on web-scale knowledge, moving toward autonomous evolving agents in open-ended tasks and environments. Extensive experiments on three long-form deep research benchmarks demonstrate that the 8B model trained via HOTE surpasses the strongest static open 8-32B models as well as those trained by state-of-the-art deep research training methods with less time overhead, and further verify that the evolution of all three modules in HOTE is indispensable.

We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a common reasoning interface. We introduce UniReason-Med, a single-checkpoint framework that processes either a 2D image or a slice-serialized 3D volume at inference time, generating interleaved textual reasoning and localized visual evidence through shared box syntax, region-token injection, and a common grounded reasoning policy. To train this interface, we construct UniMed-CoT, a 220K instruction-tuning dataset with interleaved textual reasoning and grounded visual evidence, including 170K 2D and 50K 3D samples. Through supervised fine-tuning followed by outcome-level reinforcement learning, UniReason-Med learns to generate grounded reasoning traces without IoU/Dice-based localization rewards during RL. Data-mixture and component ablations show that joint 2D+3D grounded supervision substantially improves 3D reasoning over 3D-only training, while grounding and region-token injection consistently benefit both 2D and 3D tasks. These results suggest that a shared grounded reasoning interface can transfer reasoning structure from 2D images to slice-serialized volumetric medical understanding.

Although the Universal Transformer (UT) mitigates the diminishing returns of standard LLM scaling by decoupling parameter count from depth, it remains constrained by linear computational costs and rigid weight-sharing mechanisms. These limitations lead to severe functional homogeneity, which subsequently induces over-smoothing, representation rank collapse, and degraded reasoning performance. In this work, we present the first systematic study of Compute Distribution Skew, identifying it as the primary driver of extrapolation failure. This is a pathological phenomenon in ultra-deep recurrent Transformers characterized by a disproportionate distribution of contributions across recurrent steps, resulting in distinct functional states during prefix and suffix processing phases. To address this challenge, we propose the Polymorphic Transformer, which aims to achieve functional polymorphism and depth sparsity within a shared-parameter framework. By integrating conditional sparse subspaces, SiLU Attention, and an uncertainty-aware depth scheduler, our architecture mitigates power-method collapse and effectively decouples logical depth from computational cost. Experiments demonstrate that our model significantly enhances representation rank and robustness, achieving complex reasoning performance comparable to baseline while reducing computation by 64.7%.