Cursor Reveals for the First Time: “Training Is the Product” — The Secret Weapon Using Reinforcement Learning to Make AI Coding 4× Faster

Why AI Programming Assistants Often Feel "Off"

Have you noticed that AI programming assistants are either smart but slow, or fast but inaccurate?

I wrestled with this contradiction—until Sasha Rush from Cursor presented at Ray Summit 2025.

Their team unveiled Cursor Composer, a model trained with reinforcement learning (RL) to be both highly intelligent and extremely fast.

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A New Mindset: Training-as-Product

The most important takeaway from the talk: Cursor isn’t chasing meaningless benchmark scores.

Instead, they focus on real-world programming workflows, training the model in actual codebase environments.

Key Ideas:

• RL training includes coding conventions, tool usage, and parallel execution strategies.
• The training environment is identical to the product environment—the AI "lives" the same experience as a real user.
• This training-as-product philosophy changes how AI tools should be built.

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Why “Fast and Smart” Matters

Sasha Rush showed that Cursor Composer:

• Is on par with frontier models in internal benchmarks.
• Outperforms last summer’s top mainstream releases.
• Beats all fast but less intelligent models.
• Generates tokens 4× faster than peers with similar capability.

Speed isn’t just a metric—it’s central to flow in coding.

> A 30-second AI response breaks concentration. A 2-second response keeps your brain in the zone.

The inspiration came from Cursor Tab, loved for speed and smoothness.

A prototype nicknamed Cheetah embraced this principle—fast, interactive agentic coding.

User feedback was glowing, calling it alien technology.

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The Rise of Workflow-Aware AI Tools

Beyond code, specialized AI tools now shape other industries.

Example: AiToEarn官网, an open-source platform for AI content monetization—connecting AI generation, cross-platform publishing, analytics, and model ranking.

Supported Channels:

Douyin · Kwai · WeChat · Bilibili · Rednote (小红书) · Facebook · Instagram · LinkedIn · Threads · YouTube · Pinterest · X (Twitter).

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Agent RL: Training AI to Behave Like a Real Developer

Workflow:

• User query → Cursor backend.
• Agent chooses from ~10 tools:
• Read files
• Edit files
• Search codebase
• Gather lints
• Run terminal commands
• Tools may run sequentially or in parallel.

Under the Hood:

• The agent is an LLM generating tokens.
• Tool calls follow XML-like patterns with parameters.
• Rollouts simulate multiple concurrent workflows to find the best action path.

RL Training:

• Simulates production use: real queries drive tool calls.
• Multiple rollouts from the same state test alternative action paths.
• Output quality determines parameter updates.

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Core RL Challenges

• Matching Training & Inference
• MoE models need parallel performance across thousands of GPUs.
• Must keep training and production architectures identical.
• Ultra-Long Rollouts
• Real tasks require up to 1M tokens and hundreds of tool calls.
• Example: “Refactor” may demand reading files, searching, linting, testing.
• Consistency
• Training through the real production agent ensures direct skill transfer.
• Requires identical tool formats/responses in training and production.

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Infrastructure: The Secret Weapon

Three Server Components:

• Trainer: PyTorch ML stack at extreme scale.
• Inference Server: Ray orchestration for rollouts.
• Environment Server: microVMs to simulate code editing, terminal commands, and lint checks.

Low-Precision Training:

• Custom GPU kernels for MXFP8 microscaling.
• FP8 with scaling factors → better accuracy + 3.5× MoE layer acceleration.

Rollout Heterogeneity:

• Stragglers problem solved with Ray load balancing across processes.

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Tight Product–Training Integration

Cloud agents run on the identical infrastructure as RL training—offline or even in subway rides.

Benefits:

• The model learns actual product tools, e.g., semantic search via embeddings indexing your files.
• Training produces direct, production-ready skills.

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Proof That RL Works

Early Findings:

• Steady performance gains with compute investment.
• Learned parallel tool usage → faster user experiences.
• Shift toward deliberate edits after more reading/search actions.

User feedback:

• Speed + intelligence unlock a new programming style.
• Internal devs now use Composer daily.

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Lessons: Building Specialized AI Models

Three takeaways:

• Specialization beats generalization for real tasks.
• Use your AI to build your AI — accelerates development.
• Infrastructure is essential — product and training must be tightly coupled.

Platforms like AiToEarn官网 follow similar principles—cross-platform publishing + monetization through a tightly integrated AI ecosystem.

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Conclusion: User-Centric Innovation

Cursor Composer’s success proves:

• Solving a real user pain point matters more than chasing trends.
• RL + infrastructure + product integration can deliver fast and smart AI assistants.
• The same philosophy can empower creators via ecosystems like AiToEarn官网.

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If you’d like, I can create a summary table highlighting all RL strategies, infrastructure choices, and product outcomes from Cursor Composer’s journey for quick reference. Would you like me to prepare that next?