Speculations on Test-Time Scaling (o1)

The discussion centers on advancements in language models, particularly focusing on OpenAI's latest graph illustrating the impact of test time compute on model performance. The conversation highlights the evolution from traditional scaling of language models to incorporating search and learning, as demonstrated by OpenAI's reinforcement learning techniques. Key themes include the significance of Chain of Thought processes, the integration of learned verifiers, and the potential for improved reasoning capabilities through search-based methods. The talk also explores various approaches like guess-and-check, process rewards, and advanced search algorithms such as Monte Carlo Tree Search, underscoring the importance of scaling computation and exploring new evaluation metrics for future AI research.

• OpenAI's iconic graph from the GPT-3 paper illustrates that as language model parameters increase, model performance on zero-shot tasks improves.
• A new graph from OpenAI shows that increased test time compute leads to better model performance, a novel insight in language modeling.
• The focus is on hard technical mathematical problems requiring more than pattern matching, involving full-on reasoning.

"As language model parameters get larger, the models perform better at zero-shot tasks."

• This quote highlights the correlation between larger model parameters and improved performance, a foundational concept in AI scaling.

"What we're seeing is the performance on this task get much better as we add more test time compute to the system."

• This statement underscores the novel finding that test time compute significantly enhances model performance, a new direction in AI research.

• The "Bitter Lesson" suggests that building knowledge into AI agents provides short-term gains but plateaus in the long run.
• Progress in AI often comes from scaling computation through search and learning rather than embedding knowledge.
• Recent trends indicate a shift towards using search facilitated by learning to tackle more technical problems.

"The bitter lesson is based on the historical observations that AI researchers have often tried to build knowledge into their agents."

• This quote emphasizes the traditional approach in AI research that has been challenged by the scalability of computational methods.

"What we've seen for the last 5 years is an increase in the learning capability of models."

• This statement reflects the trend of enhancing model capabilities through increased learning and computational power.

• Noan Brown's insights highlight the underestimated impact of scaling up search in AI research.
• The 2021 paper on scaling laws for board games illustrates the trade-off between training time and test time search.
• Effective AI models require balancing training and test time compute to optimize performance.

"The most important lesson is that I and other researchers simply didn't know how much of a difference scaling up search would make."

• This quote reveals the significant yet previously unappreciated role of search scaling in AI advancements.

"There is a trade-off that with more training time in the model itself, you can learn better systems."

• This statement describes the balance needed between training time and test time compute for effective AI model development.

• A 2021 OpenAI paper discussed using a learned verifier to improve model accuracy through test time compute.
• The learned verifier evaluates solutions generated by a model, enhancing accuracy beyond traditional supervised fine-tuning.
• This approach signifies a shift towards utilizing test time compute to refine and improve model outputs.

"Searching against this learned verifier can lead to improvements even upon just training on the actual good answers themselves."

• This quote highlights the effectiveness of using learned verifiers to enhance model performance beyond conventional training methods.

"This allows you to utilize that verifier to improve the model at test time."

• This statement underscores the role of learned verifiers in leveraging test time compute for model improvement.

• OpenAI's 01 model uses reinforcement learning and Chain of Thought to enhance data efficiency and model performance.
• Chain of Thought involves generating intermediate steps in problem-solving, not directly supervised but sampled from the model.
• The model's approach emphasizes data efficiency, learning from a smaller set of examples compared to traditional methods.

"Our large scale reinforcement learning algorithm teaches the model how to think productively using its Chain of Thought."

• This quote outlines the innovative use of reinforcement learning and Chain of Thought in OpenAI's model to improve problem-solving.

"The system is using reinforcement learning... requires some signal from some sort of verifiable problem."

• This statement explains the model's reliance on reinforcement learning to derive signals from problems in the absence of supervised data.

• The discussion includes a survey of public literature and insights from researchers about OpenAI's developments.
• OpenAI's model release prompts considerations about reinforcement learning, Chain of Thought, and test time compute in AI research.
• Understanding these elements can guide future research and development in AI systems, particularly in open-source contexts.

"I'll give a survey of the public literature related to opening eyes 01 as part of this process."

• This quote indicates the comprehensive exploration of OpenAI's advancements and their implications for the research community.

"The Talk itself will have four more parts: clues, technical background, suspects, and implications."

• This statement outlines the structured approach to analyzing OpenAI's model and its broader impact on AI research.

• Chain of Thought involves a model reasoning through multiple steps to arrive at an answer, resembling search and planning but not executed at test time.
• Reinforcement learning is suggested as necessary to induce this behavior in models.
• The process involves starting from a question, generating intermediate reasoning steps, and concluding with an answer.

"Chain of Thought here is providing our method of test time scaling while the actual words in the Chain of Thought look like search and planning in a classical sense."

• This highlights the role of Chain of Thought in scaling test time computation, mimicking traditional search and planning methods.

• Chain of Thought is formalized by defining a problem (X), a solution (Y), and intermediate reasoning steps (Z1 through ZT).
• The goal is to produce a distribution of answers (Y) conditioned on input (X) through a series of reasoning steps.

"We'll assume our problem specification is called X...our final solution will be called Y...a series of steps Z1 through ZT."

• This quote outlines the formal components of the Chain of Thought process, emphasizing the structure of reasoning from problem to solution.

• Ancestral sampling is used to generate reasoning steps until an answer is produced.
• Multiple chains of thought can be sampled, and a majority vote is used to determine the most common answer.
• A verifier, available only at training time, is used to check the correctness of answers.

"Specifically we'll sample steps Z...until we get to an answer Y represented by the dot on the right."

• Describes the sampling process to generate reasoning steps until reaching a conclusion.

• Automatic verifiers check answer correctness during training, influencing model improvement.
• Rejection sampling involves generating chains of thought and retaining only those verified as correct.

"We'll assume that we have an automatic verifier...we are going to utilize it as a way to provide training signal."

• Highlights the role of verifiers in training models to produce correct solutions.

• Learned verifiers can be confused by incorrect solutions, affecting model performance.
• The performance of models using learned verifiers can plateau or worsen with more samples.

"One Challenge is that with a learned verifier if the generator produces say crazy Solutions sometimes the Learned verifier gets confused."

• Addresses the difficulties and limitations of using learned verifiers in model training.

• Reinforcement learning is a complex area with many specific choices affecting system design.
• Training models for reasoning can involve using reinforcement learning to generate and refine their own Chain of Thought.

"When training a model for reasoning...train the model using RL to generate and hone its own chain of thoughts."

• Discusses the potential of reinforcement learning in enhancing model reasoning capabilities.

• Four suspects in implementing Chain of Thought: Guess and Check, Process Rewards, Search or Alpha Zero, and Learning to Correct.
• Each suspect represents a different approach or method in the literature for achieving effective reasoning in models.

"I narrowed this down to four different suspects...Guess and Check process rewards search or Alpha zero and learning to correct."

• Introduces the various approaches considered in research for implementing Chain of Thought in AI models.

• Expectation Maximization (EM) is used in reinforcement learning, involving rejection sampling and fitting models to successful samples.
• Self-training is a method where models learn from successful reasoning chains, known by different names in various research contexts.

"We can think about this as a form of rejection sampling expectation maximization EM is a very traditional algorithm."

• Explains the application of EM in refining models through successful reasoning chains and its historical context in machine learning.

• Simplification of methods in research papers shows consistent improvements in various problems, especially with lower samples.
• Emphasis on the necessity of a verifier during training to enhance the system's performance.
• Introduction of the concept of amortization, using a learned model to represent complex systems for scalability.

"This method is simple but it works and it works pretty well; you can get relatively consistent improvements, particularly in lower samples across many different problems."

• This quote highlights the effectiveness and reliability of simplified methods in improving system performance across various problems.

"We can create our own sort of learned verifier at test time; this could then be used as part of Chain of Thought or for some sort of test time rejection sampling."

• The quote discusses the potential of developing a learned verifier for test time, enhancing the system's ability to evaluate and improve its reasoning process.

• Introduction of process rewards, utilizing intermediate models to improve rejection sampling outcomes.
• Use of human annotators and rollouts to train a learned process reward model (RI).
• Potential to merge the generator and verifier into a single model for improved reasoning and verification.

"The term process rewards comes from two papers, one from Google and one from OpenAI; in these papers, they learn an early verification model which they call a PRM or process reward model."

• This quote introduces the concept of process rewards and the development of early verification models to enhance rejection sampling.

"This is an idea that merges the generator and the verifier; you can have a single model that is both trying to do reasoning and also trying to verify this reasoning."

• The quote explains the innovative approach of combining the generator and verifier into one model, improving the system's reasoning and verification capabilities.

• Discussion on AlphaZero's self-play method and its relevance to current research.
• Exploration of AlphaProof's approach to generating and verifying solutions in math competitions.
• Explanation of expert iteration, combining learned models with expert search to enhance system performance.

"In this paper, which was a follow-up to AlphaGo, they demonstrate that a system completely taught with self-play could achieve expert-level performance in a very hard task."

• This quote emphasizes the success of self-play methods in achieving expert-level performance without extensive expert demonstrations.

"The terminology for this in the literature is known as expert iteration; it refers to this iterative process where an algorithm combines a learned model plus a complex expert search."

• The quote defines expert iteration, an iterative process that refines system performance by integrating learned models with expert search.

• Examination of search algorithms like beam search and Monte Carlo Tree Search (MCTS) in language modeling.
• Use of beam search to maintain multiple solutions during the reasoning process.
• MCTS's exploration strategy to enhance problem-solving capabilities.

"While beam search is a common approach for efficiently doing search with language models, systems like AlphaGo used much more complex forms of search for gambling."

• This quote contrasts the simplicity of beam search with the complexity of MCTS, highlighting the need for robust search algorithms in advanced reasoning systems.

"Monte Carlo Tree Search is a complex algorithm that combines search with exploration; the way it works is that for a given math problem, we're going to start at the beginning."

• The quote provides an overview of MCTS, explaining its exploratory approach to solving complex problems by expanding possible solution paths.

• The expansion and rollout process involves selecting nodes, running rollouts, and updating nodes and their parents based on results.
• Selection is based on both node success and unexplored nodes, allowing diverse exploration.
• This method enables exploration of various potential chains of thought, uncovering previously unexplored paths.

"The expansion and the yellow node represent present which of the expansions we pick to roll out we then run our rollouts here I'm showing eight independent rollouts several of which reached the solution and several of which did not."

• The quote explains the process of choosing expansions and running multiple rollouts, some of which succeed in reaching a solution.

"Based on this rollout we then update the node and all of its parents to tell them how well it did."

• This highlights the importance of updating nodes and their parents with the results of rollouts to inform future decisions.

• The process fits with historical results in reinforcement learning (RL) and adds training time to the system.
• Despite being data efficient, the method likely uses substantial compute resources for training.
• The chain of thoughts resembles search properties like backtracking, suggesting training time search involvement.

"It fits with the history of major demonstrated results in RL and it's a particularly nice way of adding more training time into the system."

• This quote connects the process to established RL results and emphasizes its role in extending training time.

"Given that the chain of thoughts that we actually are seeing from 01 look a little bit like search with properties like backtracking or highle outlining it's plausible that those came into the model through something like training time search."

• The quote suggests the chain of thought's resemblance to search properties, indicating the potential influence of training time search.

• The method is algorithmically complex and costly to maintain open states, making scaling difficult.
• OpenAI's release materials do not mention this complex training tree search.
• Simpler methods may outperform this approach in language modeling, as evidenced by limited success in open research.

"It's much more complex algorithmically and it's costly to maintain open States compared to the first two systems it does seem much harder to scale."

• The quote highlights the complexity and cost of maintaining open states, posing challenges for scaling.

"We don't see anything about doing this sort of complex training Tre search in any of the open AI release material."

• This emphasizes the absence of this complex training method in OpenAI's public materials.

• Learning to correct involves isolating pairs of similar chains of thought, training models to improve incorrect ones.
• Challenges include model collapse if initial chains are poor and distribution shift issues.
• The approach aims to incorporate self-correction into the generator at scale.

"A motivating example is work on self-correction the idea here is to isolate pairs of chain of thoughts we'll call one Z Prime and the other Z double Prime these chain of thoughts are similar but one leads to a correct answer and the other does not."

• The quote explains the concept of isolating similar chains of thought to train models for self-correction.

"One issue is that the model will often just collapse if the First Chain of Thought was just not very good it'll learn to ignore it and simply just directly try to generate the second one."

• This highlights the challenge of model collapse when initial chains are not effective.

• Stream of search involves converting a tree search into a linear stream for training.
• This method allows models to see mistakes in the stream, enabling search-like behavior.
• It combines with learning to correct to improve individual steps and maintain policy adherence.

"We're going to try to convert from a tree to a stream we do tree search to explore multiple paths we'll convert this stream as a linear sequence and then we'll allow models to see their mistakes in the Stream."

• The quote describes the process of transforming tree search into a linear stream for training.

"This approach is relatively complex but a lot of experts I talked to were convinced that something like this is behind what 01 is doing."

• This indicates expert belief in the potential use of this complex approach in practice.

• Replication is crucial for the open-source community to build large-scale RL-based systems.
• Open-source versions may differ from company-designed ones but can inspire community efforts.
• The research implications include understanding test time compute and moving beyond prompting to formal specifications.

"As an open source Community we need to get better at building some of these large scale rl-based systems and showing they can really work."

• The quote emphasizes the importance of replication for the open-source community to develop effective systems.

"I'm really interested in the move from prompting to some sort of formal specification if we can produce interesting verifiers for hard problems and use language models to optimize against them that opens up all sorts of interesting new areas of work."

• This highlights the potential shift from prompting to formal specifications, opening new research avenues.

• New paths for evaluations are emerging, focusing on challenging tasks beyond current capabilities.
• Search-based systems change how models are utilized and understood, differing from traditional neural network interpretability.
• Exciting opportunities exist for understanding inference time systems and exploring superhuman evaluations.

"These models really open up many New Paths for evaluations my group has been thinking a lot about evaluations that are just extremely hard and on tasks that we'd really like to do but are way beyond the capability of even the best language models."

• The quote discusses the potential for new evaluation paths focusing on challenging tasks.

"The move to search-based systems really is about how these systems are utilized what they generate as their intermediate steps and how you might change or explore that."

• This emphasizes the shift to search-based systems and their impact on model utilization and understanding.