ทวีตที่ปักหมุด
Very excited about formula prediction being released in Google Sheets! A great collaboration between Google Sheets and Brain team.
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Rishabh Singh
65 posts
Rishabh Singh
@rishabhs
Research Lead @Databricks. Previously @Meta GenAI, Google Brain @GoogleAI, @MSFTResearch, @MIT_CSAIL @IITKgp
Mountain View, CA เข้าร่วม Haziran 2009
97 กำลังติดตาม994 ผู้ติดตาม
Rishabh Singh รีทวีตแล้ว
I’m super excited about the launch of Genie Code! It extends the power of AI coding to agentic data work, answering questions 2-3x more accurately than coding agents and automatically engineering and monitoring high quality pipelines. It’s transformed my own work at Databricks!
Databricks@databricks
Today we're announcing Genie Code, your autonomous AI partner for data. Genie Code is a state-of-the-art agent that lets data teams move from prompting a copilot to delegating real work: building pipelines, machine learning models, debugging failures, and shipping dashboards. This isn't a smarter autocomplete. It's a different kind of AI partner entirely. Unlike general coding agents that stop once the code is built, Genie Code plans, executes, and iterates across the full data and AI lifecycle inside Databricks. It's purpose-built for data engineering, data science, and BI: • More than doubles the success rate of leading coding agents on real-world data science tasks • Proactively monitors your pipelines and AI models in the background, triaging failures and fixing issues before a human intervenes • Works with your data wherever it lives, across Databricks and external platforms, with full governance and MCP support This is what the future of data work looks like. databricks.com/blog/introduci…
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Super excited about the release of Genie Code, Databricks' state of the art Data agent. The AI Research team has been collaborating closely to push the boundaries of the agent’s performance, with lot more coming soon!
Databricks@databricks
Today we're announcing Genie Code, your autonomous AI partner for data. Genie Code is a state-of-the-art agent that lets data teams move from prompting a copilot to delegating real work: building pipelines, machine learning models, debugging failures, and shipping dashboards. This isn't a smarter autocomplete. It's a different kind of AI partner entirely. Unlike general coding agents that stop once the code is built, Genie Code plans, executes, and iterates across the full data and AI lifecycle inside Databricks. It's purpose-built for data engineering, data science, and BI: • More than doubles the success rate of leading coding agents on real-world data science tasks • Proactively monitors your pipelines and AI models in the background, triaging failures and fixing issues before a human intervenes • Works with your data wherever it lives, across Databricks and external platforms, with full governance and MCP support This is what the future of data work looks like. databricks.com/blog/introduci…
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Rishabh Singh รีทวีตแล้ว
Meet KARL, an RL'd model for document-centric tasks at frontier quality and open source cost/speed. Great for @databricks customers and scientists (77-page tech report!) As usual, this isn't just one model - it's an RL assembly line to churn out models for us and our customers 🧵
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Rishabh Singh รีทวีตแล้ว
Reliable enterprise agents require system-level reasoning when retrieving across heterogeneous knowledge sources. Traditional RAG often fails to consistently follow instructions, schemas, and constraints end to end.
That’s why we’re presenting Instructed Retriever, a new retrieval architecture that propagates complete system specifications through every stage of the search pipeline. The approach delivers:
- 35–50% gains in retrieval recall on instruction-following benchmarks
- 70% improvements in end-to-end answer quality over simplistic RAG, and ~15% over reranking-based approaches
- Strong instruction adherence with small, efficient models suitable for real-world deployment
Together, these results show how system-wide instruction awareness translates directly into more accurate and efficient enterprise agents. databricks.com/blog/instructe…
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Rishabh Singh รีทวีตแล้ว
I'm hiring interns for next summer at @databricks! Specifically on (1) empirical RL at scale on non-verifiable tasks and (2) enabling real people specify the behaviors they want out of AI (e.g., through evals) on highly complex tasks. 🧵
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Rishabh Singh รีทวีตแล้ว
Big news: Databricks and @OpenAI are partnering to deliver powerful AI to the enterprise. OpenAI frontier models will now be available natively in Databricks.
This means you can build, evaluate and scale production-grade AI apps and agents on your governed enterprise data, leveraging the latest OpenAI models like GPT-5.
We’re excited to expand our relationship with OpenAI; Databricks was one of the first to host gpt-oss open models, they use Databricks products and now we’re offering OpenAI models natively on Databricks: databricks.com/blog/run-opena…
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Rishabh Singh รีทวีตแล้ว
Prompt optimization is becoming a powerful technique for improving AI that can even beat SFT! Here are some of our research results with GEPA at Databricks, in difficult Agent Bricks info extraction tasks. We can match the best models at 90x lower cost, or improve them by ~6%.
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Rishabh Singh รีทวีตแล้ว
Automated prompt optimization (GEPA) can push open-source models beyond frontier performance on enterprise tasks — at a fraction of the cost!
🔑 Key results from our research @DbrxMosaicAI:
1⃣ gpt-oss-120b + GEPA beats Claude Opus 4.1 on Information Extraction (+2.2 points) — while being 90× cheaper to serve.
2⃣ The same technique also lifts frontier models (Claude Sonnet 4, Opus 4.1), pushing them to new SOTA benchmarks.
3⃣Versus Supervised Fine-Tuning (SFT): GEPA delivers equal or better performance at 20% lower serving cost. Even better → GEPA + SFT together gives the highest gains.
4⃣Lifetime cost analysis shows GEPA + gpt-oss is orders of magnitude cheaper overall. At scale, the one-time optimization overhead fades away — making optimized agents highly practical for real-world deployment.
#dspy #gepa #promptoptimization #airesearch
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Rishabh Singh รีทวีตแล้ว
The future of data science is autonomous, collaborative, and faster than ever.
That's why we're excited to introduce the Data Science Agent for Databricks Assistant, an autonomous partner that plans, executes, and self-corrects entire workflows in your Notebooks and SQL Editor.
Get:
-End-to-end lifecycle support — from EDA to feature engineering, model training, and evaluation
-Autonomous multi-step execution with full transparency and control
-Deep Unity Catalog integration for governed, production-ready results
-Native to Databricks Notebooks and SQL Editor for a seamless experience
databricks.com/blog/introduci…
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Rishabh Singh รีทวีตแล้ว
Databricks just signed a Series K term sheet at >$100B valuation to scale two flagship products:
🔥 Lakebase — serverless Postgres with true compute/storage separation
🧠 Agent Bricks — agentic framework with built-in reasoning guardrails for enterprise data
wsj.com/tech/ai/databr…
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Rishabh Singh รีทวีตแล้ว
Try out GEPA! Excited to see how it does on people's problems.
Lakshya A Agrawal@LakshyAAAgrawal
Very excited to share that GEPA is now live on @DSPyOSS as dspy.GEPA! This is an early code release. We’re looking forward to community feedback, especially about any practical challenges in switching optimizers.
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Rishabh Singh รีทวีตแล้ว
Since joining @databricks, our research team has been hard at work on Agent Bricks, a new product that helps enterprises develop state-of-the-art domain-specific agents. We are now releasing a research blog about Agent Learning from Human Feedback (ALHF) databricks.com/blog/agent-lea…
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Rishabh Singh รีทวีตแล้ว
More details in the blog: databricks.com/blog/power-rlv…
This work was led by @DipendraMisra with contributions from many others. If you're interested in taking this for a spin yourself, sign up here: docs.google.com/forms/d/e/1FAI…
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Rishabh Singh รีทวีตแล้ว
RLVR isn't just for math and coding! At @databricks, it's impacting products and users across domains. One example: SQL Q&A. We hit the top of the BIRD single-model single-generation leaderboard with our standard TAO+RLVR recipe - the one rolling out in our Agent Bricks product.
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Rishabh Singh รีทวีตแล้ว
This is a good opportunity to announce that I recently joined the research team at @databricks where I will be working alongside @jefrankle @rishabhs @matei_zaharia Erich Elsen, and many others on the hardest problems at the intersection of information retrieval and AI.
Jonathan Frankle@jefrankle
I'm at ICML 🇨🇦 and I'm hiring at @databricks. Visit our booth if you're interested. My scientific focus: It's 1972 in AI, there's an AI crisis, Dijkstra isn't here to save us, and maybe RL can. Why Databricks? The long road to AGI is being paved here and we have the real evals 🧵
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Rishabh Singh รีทวีตแล้ว
I'm at ICML 🇨🇦 and I'm hiring at @databricks. Visit our booth if you're interested. My scientific focus: It's 1972 in AI, there's an AI crisis, Dijkstra isn't here to save us, and maybe RL can. Why Databricks? The long road to AGI is being paved here and we have the real evals 🧵
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Rishabh Singh รีทวีตแล้ว
We're finding that what's needed in RL for enterprise tasks is pretty different than in foundation model training on math, code, etc. Catch @jefrankle and our team at ICML to talk about these problems!
Jonathan Frankle@jefrankle
Properties of our problems: * Semi-verifiability. Can LLM judges productively augment RLVR? How clean must they be? * Intermediate rewards. Signals we can exploit to make harder tasks tractable. * Real traces. Tons of human traces for imitation learning or environment building.
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I'm super excited to share that I recently joined the @databricks AI research team to help with AI for data science efforts. We are working on real-world AGI to help customers succeed on the Databricks platform. We are hiring, please join us in this exciting mission!
Jonathan Frankle@jefrankle
I'm at ICML 🇨🇦 and I'm hiring at @databricks. Visit our booth if you're interested. My scientific focus: It's 1972 in AI, there's an AI crisis, Dijkstra isn't here to save us, and maybe RL can. Why Databricks? The long road to AGI is being paved here and we have the real evals 🧵
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Rishabh Singh รีทวีตแล้ว
Excited to share the project #AlphaCode I’ve been working on for more than 2 years! Can’t believe we started before COVID is a thing and worked through this project mostly at home, with an amazing team!
Google DeepMind@GoogleDeepMind
Introducing #AlphaCode: a system that can compete at average human level in competitive coding competitions like @codeforces. An exciting leap in AI problem-solving capabilities, combining many advances in machine learning! Read more: dpmd.ai/Alpha-Code 1/
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Rishabh Singh รีทวีตแล้ว
Hey, ML/PL enthusiasts! Looking for some "light" reading for the holiday break?
FnT just published our survey on "Neurosymbolic Programming", written jointly with @swarat, Kevin Ellis, @rishabhs, Armando Solar-Lezama, and @yisongyue.
nowpublishers.com/article/Detail…
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