Training prompts in online reinforcement learning (RL) differ substantially in how informative they are for the current policy: some are already saturated while others are too difficult to yield reliable learning signals, yet both receive equal rollout budget under standard training. We propose an exploration-guided prompt scaffolding framework that adapts the training prompt distribution dynamically throughout RL post-training of multimodal large language models (MLLMs). Central to our approach is the Exploration Potential Score (EPS), a lightweight rollout-based proxy for prompt utility derived from KL-regularized policy improvement theory, computable directly from on-policy rollout statistics without additional overhead. Rather than discarding low-utility prompts, we use a teacher model to generate scaffolded rewrites that preserve the original task intent while making subsequent training more informative, reframing teacher supervision as training-data refinement rather than output imitation. Integrated with GRPO on Geo3K and MMK12, our method consistently outperforms the baseline on both in-domain and out-of-distribution benchmarks, achieving up to 9.7% relative improvement in-domain and gains of 11.5% on MathVision and 11.1% on MMMU-Pro.

Production multimodal systems must serve complex vision-language tasks under strict latency, memory, and cost constraints. Frontier multimodal large language models (MLLMs) deliver strong reasoning but are prohibitively expensive for online serving; compact alternatives fit deployment budgets but lack structured reasoning ability. We present OmniThoughtVis, a scalable distillation pipeline that converts open-source multimodal corpora into deployment-ready chain-of-thought (CoT) supervision for compact MLLMs. Starting from a diverse seed pool, the pipeline generates structured reasoning traces from a high-capacity teacher, jointly annotates reasoning difficulty, answer quality, and semantic task tags, and applies difficulty-aware filtering with tag-based diversity sampling to produce a reusable 1.8M-sample candidate pool. Practitioners construct budget-specific training subsets from this pool without re-running distillation. A key practical finding is that reasoning difficulty is a far more effective data selection signal than answer quality, which becomes uninformative when teacher outputs are already strong. Distilled Qwen3-VL models (2B-8B) consistently improve over undistilled baselines across nine benchmarks, with the distilled 4B model matching or exceeding the undistilled 8B on multiple tasks. In production, replacing a large-model serving pipeline with the distilled compact model reduces end-to-end response latency by 79% while maintaining comparable downstream quality.

Small LLMs often struggle to match the agentic capabilities of large, costly models. While reinforcement learning can help, progress has been limited by two structural bottlenecks: existing open-source agentic training data are narrow in task variety and easily solved; real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes. We address these challenges with SYNTHAGENT, a framework that jointly synthesizes diverse tool-use training data and simulates complete environments. Specifically, a strong teacher model creates novel tasks and tool ecosystems, then rewrites them into intentionally underspecified instructions. This compels agents to actively query users for missing details. When handling synthetic tasks, an LLM-based user simulator provides user-private information, while a mock tool system delivers stable tool responses. For rewards, task-level rubrics are constructed based on required subgoals, user-agent interactions, and forbidden behaviors. Across 14 challenging datasets in math, search, and tool use, models trained on our synthetic data achieve substantial gains, with small models showing performance comparable to some larger baselines in certain domains.