Samaya AI拥有发布 Samaya Sterling，是经过培训后用于代理金融研究的模型。 斯特林以NVIDIA的"尼莫特龙3.5闪电"(Nemotron 3.5 Lightning)为基础，是30B型混合型专家模型，有3B活性参数。 在萨马亚的"边境金融基准测试"上，它的得分是58.9%. Claude 第5.1号寓言的得分是55.9%. 每个斯特林问题费用为0.61美元，而Fable 5.1问题费用为6.89美元。 对于开发者来说，外卖是一种具体的食谱：用它的取材来共同训练一个小模型，它可以在狭义的域中取胜。
TL; DR 数据
- 大小：共计30B,3B活动(MOE)。 基础模型上下文：最多1M个符。 斯特林的服务背景：未披露。
- 运行在：基型符合BF16(256K上下文)中单H100 80GB. 斯特林的服务硬件：没有披露。
- 业绩：在Frontier Finance领导板上以每美元的质量(自报)击败每个高质量的系统。
- 最佳：边境金融58.9%(高功率模式为60.6%),Claude有线5.1为55.9%。
- 最糟糕的是：关于边境金融问题最困难的三分之一为45.2%。
什么是萨马亚·斯特林？
Samaya Sterling是Nemotron 3.5闪电的LoRA后训练版本，调制为金融研究agent. 它计划研究，调用金融工具，收集证据，将调查结果交给一个单独的报告撰写人。 根据Maithra Raghu的X声明
吊带如何支持3B活性模型？
萨马亚团队调整了自己的生产融资手段，以用于一个小型模型。 从系统即时和工具说明中删除了详细的指导。 合并了工具以缩短提示时间。 两个助手模型管理上下文。 收缩器能缩短长的工具结果。 一个收缩器总结了追踪随着它的生长。 一个研究预算上限 agent工作时间长。 之后，报告撰写者从所发现的东西中解答。
斯特林是怎么训练出来的？
训练使用LoRA在叮当上，思考机器实验室的训练API. 它分为四个阶段：
- 关于合成痕迹的SFT:Samaya产生了968个合成分析师问题。 老师Kimi K3各答了两次，LLM法官选择了更好的答案。 该模型学会了调用37个有有效论据的工具。
- SFT在8个战略中的最佳：Kimi K3用8个研究战略回答了1 819个专家写的问题。 每个问题得到的最好答复成为培训数据。 " 最佳8 " 满足了80%的基本项目，而 " 最佳战略 " 则达到约66%。 经过过滤，仍有1 157个问题。
——离线GRPO:GRPO打出所有8名老师对接。 不需要新一代，所以每一步都便宜。
- Online GRPO：模特儿回答每个问题8次，从分级尝试中吸取教训。 短重采样纠正了已知的MOE采样器不匹配。
斯特林在边境金融方面得分如何？
Frontier Finance有220个公开问询得分与专家写的题目。 分级员使用3个型号的法官座板。 Samaya小组说，在训练中没有使用基准测试问题。 就100个内部验证问题选定了检查站。
斯特林的分数58.9%，每题0.61分和282分。 寓言5.1以6.89分和268 s. Samaya自己的高功率系统得分55.9%，以1.81分的成绩低了2.9分。
每个阶段都增加了质量：42.5%(基准),51.4%,54.8%,57.5%，再58.9%。 中度工具电话从3个增加到17个。 在高功率模式下，斯特林以1.83分和372分之多达到60.6%. 在最困难的三分之一，从31.8%上升到45.2%。
模型本身每问题花费0.04美元。 工具与辅助模型组成了0.61的其余部分。
斯特林怎么比较？
所有栏目的边际金融数字来自Samaya博客
关键外卖
- Sterling在Frontier Finance上得分58.9%，比Claude Fable 5.1高3.0分。
- 每个问题的费用减少11x:0.61美元对6.89美元。
- 3B活性参数，训练有2个SFT和2个GRPO相。
- 驾驭和模式协同训练推动收益。
- 所有结果均在Samaya自己的基准测试上自行报告。
请检查access-date=中的日期值 (帮助) 技术博客"Frontier Finance 基准测试"，数据集和分级回放。 并加入我们的通讯。 等等，你在电报里吗？现在你也可以加入我们的电报。
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Samaya AI has released Samaya Sterling, a post-trained model for agentic financial research. Sterling is built on NVIDIA’s Nemotron 3.5 Lightning, a 30B mixture-of-experts model with 3B active parameters. On Samaya’s FrontierFinance benchmark, it scores 58.9%. Claude Fable 5.1 scores 55.9%. Each Sterling question costs $0.61, against $6.89 for Fable 5.1. For developers, the takeaway is a concrete recipe: co-train a small model with its harness, and it can win in a narrow domain.
TL;DR
- Size: 30B total, 3B active (MoE). Base model context: up to 1M tokens. Sterling’s serving context: not disclosed.
- Runs on: the base model fits a single H100 80GB in BF16 (256K context). Sterling’s serving hardware: not disclosed.
- Performance: beats every high-quality system on the FrontierFinance leaderboard on quality per dollar (self-reported).
- Best: 58.9% on FrontierFinance (60.6% in high-effort mode), vs 55.9% for Claude Fable 5.1.
- Worst: 45.2% on the hardest third of FrontierFinance questions.
What is Samaya Sterling?
Samaya Sterling is a LoRA post-trained version of Nemotron 3.5 Lightning, tuned to work as a financial research agent. It plans research, calls finance tools, gathers evidence and hands findings to a separate report writer. According to Maithra Raghu’s X announcement, it started as a 2-person effort on agentic orchestration efficiency.
How does the harness support a 3B-active model?
Samaya team adapted its production finance harness for a small model. Detailed guidance was stripped from the system prompt and tool descriptions. Tools were merged to shorten the prompt. Two helper models manage context. A condenser shortens long tool results. A compactor summarizes the trace as it grows. A research budget caps how long the agent works. After that, the report writer answers from what was found.
How was Sterling trained?
Training used LoRA on Tinker, Thinking Machines Lab’s training API. It ran in 4 stages:
- SFT on synthetic traces: Samaya generated 968 synthetic analyst questions. The teacher, Kimi K3, answered each twice, and an LLM judge picked the better answer. The model learned to call 37 tools with valid arguments.
- SFT on the best of 8 strategies: Kimi K3 answered 1,819 expert-written questions using 8 research strategies. The best-graded answer per question became training data. Best-of-8 met 80% of essential rubric items, against about 66% for the single best strategy. After filtering, 1,157 questions remained.
- Offline GRPO: GRPO scored all 8 teacher answers against each other. No new generation was needed, so each step was cheap.
- Online GRPO: The model answered each question 8 times and learned from graded attempts. Truncated importance sampling corrected a known MoE sampler mismatch.
How does Sterling score on FrontierFinance?
FrontierFinance has 220 public queries scored against expert-written rubrics. The grader uses a 3-model judge panel. Samaya team states that no benchmark question was used in training. Checkpoints were chosen on 100 internal validation questions.
Sterling scores 58.9% at $0.61 and 282 s per question. Fable 5.1 scores 55.9% at $6.89 and 268 s. Samaya’s own high-effort system scores 2.9 points lower at $1.81.
Every stage added quality: 42.5% (base), 51.4%, 54.8%, 57.5%, then 58.9%. Median tool calls rose from 3 to 17. In high-effort mode, Sterling reaches 60.6% at $1.83 and 372 s. On the hardest third, it rose from 31.8% to 45.2%.
The model itself costs $0.04 per question. Tools and helper models make up the rest of the $0.61.
How does Sterling compare?
FrontierFinance figures for all columns come from the Samaya blog and are self-reported.
Key Takeaways
- Sterling scores 58.9% on FrontierFinance, 3.0 points above Claude Fable 5.1.
- Cost per question drops 11x: $0.61 vs $6.89.
- 3B active parameters, trained with 2 SFT and 2 GRPO stages.
- Harness and model co-training drive the gains.
- All results are self-reported on Samaya’s own benchmark.
Check out the technical blog, the FrontierFinance benchmark, the dataset and the grader repo. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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