Ripple-65B Preview 刷新美国开源模型 Terminal-Bench 2.1 成绩
@StanfordAILab’s @talaei_shayan shipping incredible work! 🚀
五人小团队从零训了个 65B 稠密模型，拿了美国开源在 Terminal-Bench 2.1 的最好成绩，训练细节写得挺实在。
RicursiveAI 的 Shayan Talaei 团队发布 Ripple-65B Preview，由 5 名研究员从零训练，在 Terminal-Bench 2.1 的智能体编程任务上创下美国开源权重模型最好成绩。该模型刻意采用稠密架构而非 MoE，以降低长工具调用序列下的延迟并简化训练与推理栈。团队未购买任何后训练数据，全部通过自建数据生成与过滤流水线完成，训练依赖开源框架。RL 阶段采用混合强化学习，通过按领域调整过采样比例控制批量延迟，并配合分数中心化与截断重要性采样保持训练稳定。
@StanfordAILab’s @talaei_shayan shipping incredible work! 🚀
@StanfordAILab ’s @talaei_shayan shipping incredible work! 🚀 Shayan Talaei @talaei_shayan Really happy to finally share Ripple-65B Preview! We trained it from scratch with a core team of five researchers, and it sets a new U.S. open-weight SOTA on Terminal-Bench 2.1 for agentic coding. Three choices that shaped how we built it, and a few things I learned along the way. 1. We deliberately chose a dense model. Many leading open-weight models are MoEs. For our use case, we wanted strong reasoning and low latency for agents going through long sequences of model and tool calls. Going dense also gave us a simpler training and serving stack and avoided expert-routing communication. That combination mattered a lot to us. 2. We bought no posttraining data. A lot of the work went into building our own data generation and filtering pipelines, and making sure we covered the tasks we cared about. We built on open-source training and inference stacks throughout. I’m particularly happy about how much we were able to do as a small team by building on the community’s work. 3. We worked on the RL system and curriculum together. Mixed RL meant dealing with very different rollout lengths, tool-call patterns and shares of the batch across domains. We tuned oversampling per domain and per problem to keep batch latency down, while managing policy staleness and trainer-inference mismatch. Score centering worked best among the corrections we tried; we used it alongside truncated importance sampling. We also kept adjusting task selection and introducing harder problems as the model improved. Getting batches ready faster and keeping updates stable had to go hand in hand with giving the model useful problems to learn from. Loved building this with @DZhang50 , @esonghori2 , @ethanboneh and @amiwilliamhuang . Huge thanks to our cofounders @annadgoldie and @Azaliamirh , and the whole @RicursiveAI family for making it possible. Really proud of what we built together. 🔗 View Quoted Tweet 💬 1 🔄 0 ❤️ 2 👀 282 📊 1 ⚡
