Nathan's @cursor_ai team didn't prompt-engineer their way to Composer 2.5. They trained it. The mass...
TL;DR · AI Summary
The Cursor team achieved Composer 2.5 through reinforcement learning training rather than prompt engineering, with their large-scale RL program running inference on Fireworks, indicating that self-trained models will be the only way to maintain competitive moats after 2027.
Key Takeaways
- The Cursor team used reinforcement learning to train Composer 2.5 instead of pro
- Large-scale RL programs run RL rollout and production inference on the Fireworks
- Self-trained models will be the only way to maintain competitive advantages afte
Outline
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The Cursor team trained Composer 2.5 through reinforcement learning rather than using prompt engineering methods.
Large-scale reinforcement learning programs run RL rollout and production inference on the Fireworks platform.
By 2027 and beyond, training your own models will become the only way to maintain competitive moats.
Mindmap
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- Cursor Composer 2.5训练策略
- 训练方法
- 强化学习训练
- 非提示工程
- 基础设施
- Fireworks平台
- RL rollout + 推理
- 未来展望
- 2027+自训练模型
- 竞争护城河
Highlights
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Nathan's @cursor_ai team didn't prompt-engineer their way to Composer 2.5. They trained it.
The massive RL program runs RL rollouts on Fireworks, alongside production inference.
Training your own model → the only way to sustain a competitive moat in 2027+
"Comment 🔥 to see my prompt" → may work for influencers (in 2025)
Training your own model → the" / X
Nathan's
team didn't prompt-engineer their way to Composer 2.5. They trained it. The massive RL program runs RL rollouts on Fireworks, alongside production inference. "Comment 
to see my prompt" → may work for influencers (in 2025) Training your own model → the only way to sustain a competitive moat in 2027+ Learn more → fireworks.ai/training-rl-ro
Quote
@ntindall_
May 18
Excited to launch Composer 2.5 today! Huge shoutout to the team at @FireworksAI_HQ for being a fantastic partner throughout the process. x.com/cursor_ai/stat…