Rishabh Tiwari @rish2k1
RS Intern @Meta | CS PhD @UCBerkeley | Ex-@GoogleAI | Research area: Efficient and robust AI systems rishabhtiwari.org Berkeley, CA Joined May 2019-
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Feel like I'm taking crazy pills. We are just back at step one. Don’t store KV cache, just recompute it.
Feel like I'm taking crazy pills. We are just back at step one. Don’t store KV cache, just recompute it.
Can we break the memory wall for LLM inference via KV cache rematerialization? 🚨 Introducing XQuant, which leverages underutilized compute units to eliminate the memory bottleneck for LLM inference! • 10–12.5x memory savings vs. FP16 • Near-zero accuracy loss • Beats…
Heading to @COLM_conf in Montreal? So is @WiMLworkshop! 🎉 We are organizing our first ever event at #CoLM2025 and we want you to choose the format! What excites you the most? Have a different idea? Let us know in the replies! 👇 RT to spread the word! ⏩
How does prompt optimization compare to RL algos like GRPO? GRPO needs 1000s of rollouts, but humans can learn from a few trials—by reflecting on what worked & what didn't. Meet GEPA: a reflective prompt optimizer that can outperform GRPO by up to 20% with 35x fewer rollouts!🧵
Meows, music, murmurs and more! We train a general purpose audio encoder and open source the code, checkpoints and evaluation toolkit.
Meows, music, murmurs and more! We train a general purpose audio encoder and open source the code, checkpoints and evaluation toolkit.
If you are at ICML this year, make sure to catch @rish2k1 at the Efficient Systems for Foundation Models Workshop at east exhibition hall A to learn more about our work on accelerating test-time scaling methods to achieve better latency/accuracy tradeoffs!
If you are at ICML this year, make sure to catch @rish2k1 at the Efficient Systems for Foundation Models Workshop at east exhibition hall A to learn more about our work on accelerating test-time scaling methods to achieve better latency/accuracy tradeoffs!
Really interesting work by my friend @Harman26Singh on making reward models more robust, effectively reducing reliance on spurious attributes
Really interesting work by my friend @Harman26Singh on making reward models more robust, effectively reducing reliance on spurious attributes
If you had 15min to tell thousands of Berkeley CS/Data/Stats grads what to do with their lives, what would you say? Last Thursday I told them to RUN AT FAILURE. Afterwards, while we were shaking hands & taking selfies, hundreds of them told me that they are excited to go fail. I…
Excited to share that our paper Quantspec has been accepted to #ICML2025! Huge thanks to my collaborators! Paper: arxiv.org/abs/2502.10424
Excited to share that our paper Quantspec has been accepted to #ICML2025! Huge thanks to my collaborators! Paper: arxiv.org/abs/2502.10424
Checkout our new paper 🚀: “Why Do Multi-Agent LLM Systems Fail?” Paper link: arxiv.org/abs/2503.13657… Code: github.com/multi-agent-sy… Website: sites.google.com/berkeley.edu/m… 1/n
1/6❓Can robots learn to play competitive sports by observing expert human players? 🚨Introducing LATTE-MV, a scalable 3D reconstruction system that⚙️800+ hrs of Youtube videos to create the largest 3D 🏓TT dataset! ✅27 hrs of gameplay | 73k exchanges 🌐sastry-group.github.io/LATTE-MV/
So much discussion about LLMs changing software dev - our award winning CHI paper shows how LLMs are transforming the precursor step – software prototyping. LLMs are breaking silos and dramatically reshaping the roles and collaboration of PM, UX, and developers. 1/
Why Do Multi-Agent LLM Systems “still” Fail? A new study explores why Multi Agent Systems are not significantly outperforming single-agent. The study identifies 14 failure modes multi-agent system. Multi-agent system (MAS) are agents that interact, communicate, and collaborate to…
🧵Introducing LangProBe: the first benchmark testing where and how composing LLMs into language programs affects cost-quality tradeoffs! We find that, on avg across diverse tasks, smaller models within optimized programs beat calls to larger models at a fraction of the cost.

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