Reinforcement learning with verifiable rewards (RLVR) improves mathematical reasoning, but sparse sequence-level rewards make training heavily dependent on long, repeated rollout generation. On-policy distillation (OPD) makes these rollouts more informative by asking a larger teacher to score every student trajectory token by token, but this inserts large-teacher forward passes into every RL step and requires online resources beyond standard student-only RL. We propose SPEC-Distill, a speculative prefix distillation framework in which the teacher shapes rollout construction rather than defining an auxiliary reward. Instead of querying the teacher during training, SPEC-Distill runs it once offline to build a reusable pool of expert trajectories. During RL, the student treats these trajectories as speculative drafts, accepts a prefix under a lenience-controlled criterion, completes the response under its own policy, and is updated only by verifiable rewards with standard GRPO. Against OPD, this removes online teacher scoring; against standard autoregressive rollouts, accepted prefixes reduce the number of fresh student-generated tokens. In the primary non-think setting, SPEC-Distill improves Qwen3-4B from 66.1 to 73.2 average accuracy over GRPO, and outperforms OPD on the two scales. Compared with full-response expert reuse such as Luffy, SPEC-Distill reaches comparable accuracy on Qwen3-1.7B while reducing online rollout tokens by about 60% and rollout time from 10.90 to 7.06 hours. Analysis further shows that teacher prefixes are best used as an early scaffold, after which reusing the student’s own rollouts provides the strongest balance between expert guidance and on-policy exploration.

Long audio-video reasoning is difficult for omni-modal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs. We introduce OmniReasoner, a tool-use post-training framework for Thinking with Long Audio-Video: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering. OmniReasoner first builds a low-cost global preview of the full stream and then, when needed, calls the zoom-in tool with a requested temporal interval for higher-fidelity visual and audio inspection before answering. Because the model observes different sampling granularities before and after this call—a sparse global preview and a denser local clip—we introduce TimeAnchor, which keeps the tool’s temporal argument valid and round-trip-consistent across these granularities, rather than tied to frame indices from a particular sampling rate. To make this tool-use behavior trainable without expensive manual interval annotation, we build a Temporal Augmented Data Engine that synthesizes tool-use post-training trajectories by video editing and composition. Experiments across omni-modal and video benchmarks show that OmniReasoner improves both answer accuracy and temporal grounding while concentrating high-fidelity computation on informative regions. Code is available at https://github.com/RockyChen0205/OmniReasoner.

End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation. This method can not only match the relevant facts for the input utterance but diffuse them to similar entities. With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base. Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats. The experiment results also show that our model outperforms competitive baseline models significantly.