Ripple-65B Preview 发布：美国开源权重智能体编码 SOTA 模型

Ripple-65B Preview: U.S. Open-Weight SOTA on Agentic Coding

AI 导读

Ripple-65B Preview 是一款专注于智能体编码的美国开源权重模型，在 TerminalBench 2.1 上取得了 80.5 的得分。该模型未依赖外部后训练数据，而是采用自主研发的合成数据管线与高效的强化学习方案，以极少的 rollout 实现了前沿性能。

正文 · 原文

Rather than investing in a brute-force RL climb, we carefully iterated on our SFT and RL recipes with a focus on compute efficiency. We opted not to buy any posttraining data, instead devising our own synthetic data pipelines, quality filtering, and coverage metrics.

Our mixed RL recipe uses

truncated importance sampling

score centering

, and

linear length penalty

with high-quality long-horizon synthetic environments. The RL tasks are tuned for our model capabilities, and we continuously added harder tasks as the model improved.

The end result was a steep RL climb from a strong initial SFT checkpoint. We reached a TerminalBench 2.1 score of 80.5 with only 1.4M rollouts, roughly 70x fewer rollouts than Reflection Beam, the prior SOTA.