Rohan Paul@rohanpaul_ai · X仅1000万参数的GRAM模型，通过引入可学习的随机性，在推理时并行探索多条不同路径，打破了传统递归模型锁定单一思维的限制。该模型在测试时同时运行这些平行轨迹，并借助奖励预测器选择最优结果，从而在深度之上增加了“宽度”维度。实验表明，GRAM在困难数独任务上准确率高达97%，远超此前最佳确定性模型；在多解的皇后问题上也能维持高性能，并能高效生成有效的数独谜题。这一框架为提升小模型的推理能力提供了新思路。
A 10 million parameter model just outperformed deterministic rivals 3 times its size by doing something regular recursive AI dont do： exploring multiple reasoning paths at the same time.
Most AI reasoning models are trapped on a single train of thought， and GRAM （"Generative Recursive Reasoning"） is the first to break that by letting the model think in parallel universes simultaneously.
The problem is that all existing recursive models are fully deterministic， meaning given the same input they always follow the exact same reasoning path and can never escape a wrong trajectory or discover more than 1 valid answer.
GRAM fixes this by injecting learned randomness at each refinement step， so the model samples a slightly different direction each time rather than snapping to 1 fixed next state， which produces a spread of diverse reasoning trajectories.