LLMs are democratizing digital intelligence, but we’re all waiting for AI agents to take this to the next level by planning tasks and executing actions to actually transform the way we work and live our lives.
Yet despite incredible hype around AI agents, we’re still far from that “tipping point” with best in class models today. As one measure: coding agents are now scoring in the high-teens % on the SWE-bench benchmark for resolving GitHub issues, which far exceeds the previous unassisted baseline of 2% and the assisted baseline of 5%, but we’ve still got a long way to go.
Why is that? What do we need to truly unlock agentic capability for LLMs? What can we learn from researchers who have built both the most powerful agents in the world, like AlphaGo, and the most powerful LLMs in the world?
To find out, we’re talking to Misha Laskin, former research scientist at DeepMind. Misha is embarking on his vision to build the best agent models by bringing the search capabilities of RL together with LLMs at his new company, Reflection AI. He and his cofounder Ioannis Antonoglou, co-creator of AlphaGo and AlphaZero and RLHF lead for Gemini, are leveraging their unique insights to train the most reliable models for developers building agentic workflows.
Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital
00:00 Introduction
01:11 Leaving Russia, discovering science
10:01 Getting into AI with Ioannis Antonoglou
15:54 Reflection AI and agents
25:41 The current state of Ai agents
29:17 AlphaGo, AlphaZero and Gemini
32:58 LLMs don’t have a ground truth reward
37:53 The importance of post-training
44:12 Task categories for agents
45:54 Attracting talent
50:52 How far away are capable agents?
56:01 Lightning round
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