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OpenAI's Noam Brown, Ilge Akkaya and Hunter Lightman on o1 and Teaching LLMs to Reason Better
Combining LLMs with AlphaGo-style deep reinforcement learning has been a holy grail for many leading AI labs, and with o1 (aka Strawberry) we are seeing the most general merging of the two modes to date. o1 is admittedly better at math than essay writing, but it has already achieved SOTA on a number of math, coding and reasoning benchmarks.
Deep RL legend and now OpenAI researcher Noam Brown and teammates Ilge Akkaya and Hunter Lightman discuss the ah-ha moments on the way to the release of o1, how it uses chains of thought and backtracking to think through problems, the discovery of strong test-time compute scaling laws and what to expect as the model gets better.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned in this episode:
Learning to Reason with LLMs: Technical report accompanying the launch of OpenAI o1.
Generator verifier gap: Concept Noam explains in terms of what kinds of problems benefit from more inference-time compute.
Agent57: Outperforming the human Atari benchmark, 2020 paper where DeepMind demonstrated “the first deep reinforcement learning agent to obtain a score that is above the human baseline on all 57 Atari 2600 games.”
Move 37: Pivotal move in AlphaGo’s second game against Lee Sedol where it made a move so surprising that Sedol thought it must be a mistake, and only later discovered he had lost the game to a superhuman move.
IOI competition: OpenAI entered o1 into the International Olympiad in Informatics and received a Silver Medal.
System 1, System 2: The thesis if Danial Khaneman’s pivotal book of behavioral economics, Thinking, Fast and Slow, that positied two distinct modes of thought, with System 1 being fast and instinctive and System 2 being slow and rational.
AlphaZero: The predecessor to AlphaGo which learned a variety of games completely from scratch through self-play. Interestingly, self-play doesn’t seem to have a role in o1.
Solving Rubik’s Cube with a robot hand: Early OpenAI robotics paper that Ilge Akkaya worked on.
The Last Question: Science fiction story by Isaac Asimov with interesting parallels to scaling inference-time compute.
Strawberry: Why?
O1-mini: A smaller, more efficient version of 1 for applications that require reasoning without broad world knowledge.
00:00 - Introduction
01:33 - Conviction in o1
04:24 - How o1 works
05:04 - What is reasoning?
07:02 - Lessons from gameplay
09:14 - Generation vs verification
10:31 - What is surprising about o1 so far
11:37 - The trough of disillusionment
14:03 - Applying deep RL
14:45 - o1’s AlphaGo moment?
17:38 - A-ha moments
21:10 - Why is o1 good at STEM?
24:10 - Capabilities vs usefulness
25:29 - Defining AGI
26:13 - The importance of reasoning
28:39 - Chain of thought
30:41 - Implication of inference-time scaling laws
35:10 - Bottlenecks to scaling test-time compute
38:46 - Biggest misunderstanding about o1?
41:13 - o1-mini
42:15 - How should founders think about o1?
View all episodes
By Sequoia Capital
4.5
2626 ratings
Combining LLMs with AlphaGo-style deep reinforcement learning has been a holy grail for many leading AI labs, and with o1 (aka Strawberry) we are seeing the most general merging of the two modes to date. o1 is admittedly better at math than essay writing, but it has already achieved SOTA on a number of math, coding and reasoning benchmarks.
Deep RL legend and now OpenAI researcher Noam Brown and teammates Ilge Akkaya and Hunter Lightman discuss the ah-ha moments on the way to the release of o1, how it uses chains of thought and backtracking to think through problems, the discovery of strong test-time compute scaling laws and what to expect as the model gets better.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned in this episode:
Learning to Reason with LLMs: Technical report accompanying the launch of OpenAI o1.
Generator verifier gap: Concept Noam explains in terms of what kinds of problems benefit from more inference-time compute.
Agent57: Outperforming the human Atari benchmark, 2020 paper where DeepMind demonstrated “the first deep reinforcement learning agent to obtain a score that is above the human baseline on all 57 Atari 2600 games.”
Move 37: Pivotal move in AlphaGo’s second game against Lee Sedol where it made a move so surprising that Sedol thought it must be a mistake, and only later discovered he had lost the game to a superhuman move.
IOI competition: OpenAI entered o1 into the International Olympiad in Informatics and received a Silver Medal.
System 1, System 2: The thesis if Danial Khaneman’s pivotal book of behavioral economics, Thinking, Fast and Slow, that positied two distinct modes of thought, with System 1 being fast and instinctive and System 2 being slow and rational.
AlphaZero: The predecessor to AlphaGo which learned a variety of games completely from scratch through self-play. Interestingly, self-play doesn’t seem to have a role in o1.
Solving Rubik’s Cube with a robot hand: Early OpenAI robotics paper that Ilge Akkaya worked on.
The Last Question: Science fiction story by Isaac Asimov with interesting parallels to scaling inference-time compute.
Strawberry: Why?
O1-mini: A smaller, more efficient version of 1 for applications that require reasoning without broad world knowledge.
00:00 - Introduction
01:33 - Conviction in o1
04:24 - How o1 works
05:04 - What is reasoning?
07:02 - Lessons from gameplay
09:14 - Generation vs verification
10:31 - What is surprising about o1 so far
11:37 - The trough of disillusionment
14:03 - Applying deep RL
14:45 - o1’s AlphaGo moment?
17:38 - A-ha moments
21:10 - Why is o1 good at STEM?
24:10 - Capabilities vs usefulness
25:29 - Defining AGI
26:13 - The importance of reasoning
28:39 - Chain of thought
30:41 - Implication of inference-time scaling laws
35:10 - Bottlenecks to scaling test-time compute
38:46 - Biggest misunderstanding about o1?
41:13 - o1-mini
42:15 - How should founders think about o1?
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