Molecular design is a central task in drug discovery that requires precise structural reasoning and domain knowledge. While large language models (LLMs) have shown promise in generating high-level editing intentions in natural language, they often struggle to faithfully execute these modifications, particularly when operating on non-intuitive representations like SMILES. We introduce MECo, a framework that bridges reasoning and execution by translating editing actions into executable code. MECo reformulates molecular modification for LLMs as a cascaded framework: generating human-interpretable editing intentions from a molecule and property goal, followed by translating those intentions into executable structural edits via code generation. Our approach achieves over 98% accuracy in reproducing held-out realistic edits derived from chemical reactions and target-specific compound pairs. On downstream benchmarks spanning physicochemical properties and target activities, MECo substantially improves consistency by 38-86 percentage points to 90%+ and achieves higher success rates over SMILES-based baselines while preserving structural similarity. By aligning intention with execution, MECo enables consistent, controllable and interpretable molecular design, laying the foundation for high-fidelity feedback loops and collaborative human–AI workflows in drug discovery.

Test-time scaling improves the reasoning capability of large language models by generating longer sequential chains of thought. While effective, this approach reaches a bottleneck as computation increases, resulting in a “test-time scaling plateau” where further computation provides only marginal gains. We argue that this ceiling reflects a limitation of the scaling strategy rather than model capability, a phenomenon we term Tunnel Vision, where imperfect early steps can lock the model into a suboptimal reasoning path. Motivated by this observation, we propose ParaThinker, an end-to-end parallel reasoning framework that trains LLMs to generate multiple reasoning paths in parallel and aggregate them into a final answer within a single architecture. By exploring different lines of thought, ParaThinker sidesteps the Tunnel Vision issue and unlocks the model’s reasoning potential. On challenging reasoning benchmarks, ParaThinker achieves accuracy improvements (e.g., 6.5%–20.7% on AIME-24 with a 1.5B model) over sequential-reasoning LLMs under the same budget of decoding tokens. Under matched decoding token budgets and with KV-cache reuse, ParaThinker achieves 38.7%–66.8% lower inference latency than sequential decoding via batched parallel generation. The code is available at https://github.com/MobileLLM/ParaThinker.

Scaling test-time computation improves the reasoning ability of large language models (LLMs), but the resulting latency and cost limit its use in latency- and cost-sensitive scenarios. We present BudgetThinker, a framework for reasoning-budget adherence that provides online control of chain-of-thought (CoT) length during decoding. Rather than relying on a one-shot prompt instruction, BudgetThinker periodically inserts specialized tokens that communicate the remaining reasoning budget. A two-stage training pipeline, consisting of supervised fine-tuning and curriculum-based reinforcement learning, teaches the model to interpret these signals and adapt its reasoning pace. Across the evaluated 1.5B and 7B text backbones, a 7B multimodal backbone, and a 1.5B code-reasoning model evaluated on HumanEval, BudgetThinker improves budget adherence and accuracy–efficiency trade-offs over competitive baselines. The controlled comparisons isolate the contribution of online ratio signaling from the shared training pipeline. Code is available at https://github.com/MobileLLM/BudgetThinker.

Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited: Supervised Fine-Tuning (SFT) remains constrained by data saturation and performance ceilings, while Reinforcement Learning with Verifiable Reward (RLVR), though successful in verifiable domains like math and code, cannot be directly migrated to open-ended long-form writing due to a lack of ground-truths. To further advance long-form writing, we present Writing-RL: an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT. The framework consists of three key components: Margin-aware Data Selection strategy that prioritizes samples with high learning potential, Pairwise Comparison Reward mechanism that provides discriminative learning signals in the absence of verifiable rewards, and Dynamic Reference Scheduling approach, which plays a critical role by adaptively adjusting task difficulty based on evolving model performance. Experiments on 7B-scale writer models show that Writing-RL effectively improves long-form writing performance over strong SFT baselines. Furthermore, we observe that models trained with long-output RL generalize surprisingly well to long-input reasoning tasks, potentially offering a promising perspective for rethinking long-context training.

Data-generation based zero-shot learning, although effective in training Small Task-specific Models (STMs) via synthetic datasets generated by Pre-trained Language Models (PLMs), is often limited by the low quality of such synthetic datasets. Previous solutions have primarily focused on single PLM settings, where synthetic datasets are typically restricted to specific sub-spaces and often deviate from real-world distributions, leading to severe distribution bias. To mitigate such bias, we propose FuseGen, a novel data-generation based zero-shot learning framework that introduces a new criteria for subset selection from synthetic datasets via utilizing multiple PLMs and trained STMs. The chosen subset provides in-context feedback to each PLM, enhancing dataset quality through iterative data generation. Trained STMs are then used for sample re-weighting as well, further improving data quality. Extensive experiments across diverse tasks demonstrate that FuseGen substantially outperforms existing methods, highly effective in boosting STM performance in a PLM-agnostic way. The code is available at https://github.com/LindaLydia/FuseGen.