Reinforcement LearningThe reinforcement learning stage uses a large and diverse prompt distribution spanning mathematics, coding, STEM reasoning, web search, and tool usage across both single-turn and multi-turn environments. Rewards are derived from a combination of verifiable signals, such as correctness checks and execution results, and rubric-based evaluations that assess instruction adherence, formatting, response structure, and overall quality. To maintain an effective learning curriculum, prompts are pre-filtered using open-source models and early checkpoints to remove tasks that are either trivially solvable or consistently unsolved. During training, an adaptive sampling mechanism dynamically allocates rollouts based on an information-gain metric derived from the current pass rate of each prompt. Under a fixed generation budget, rollout allocation is formulated as a knapsack-style optimization, concentrating compute on tasks near the model's capability frontier where learning signal is strongest.
她们比任何人都懂得：看懂人心，本身就是一种顶级的科技变现。，这一点在新收录的资料中也有详细论述
США впервые ударили по Ирану ракетой PrSM. Что о ней известно и почему ее назвали «уничтожителем» российских С-400?20:16，更多细节参见新收录的资料
Scoping out the exploit。关于这个话题，新收录的资料提供了深入分析