Q* explained: Complex Multi-Step AI Reasoning
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- Опубликовано: 28 июн 2024
- NEW Q* explained: Complex Multi-Step AI Reasoning for Experts only (integrating graph theory and Q-learning from reinforcement learning of LLMs and VLMs).
My video provides an in-depth analysis of Q-Star, a novel approach that amalgamates Q-Learning and A-Star algorithms to address the challenges faced by large language models (LLMs) in multi-step reasoning tasks. This approach is predicated on conceptualizing the reasoning process as a Markov Decision Process (MDP), where states represent sequential reasoning steps and actions correspond to subsequent logical conclusions. Q-Star employs a sophisticated Q-value model to guide decision-making, estimating future rewards and optimizing policy choices to enhance the accuracy and consistency of AI reasoning.
Integration of Q-Learning and A-Star in Q-Star
Q-Star's methodology leverages the strengths of both Q-Learning and A-Star. Q-Learning's role is pivotal in enabling AI agents to navigate through a decision space by learning optimal actions through reward feedback, facilitated by the Bellman equation. Conversely, A-Star contributes its efficient pathfinding capabilities, ensuring optimal decision pathways are identified with minimal computational waste. Q-Star synthesizes these functionalities to form a robust framework that improves the LLM's ability to navigate complex reasoning tasks effectively.
Practical Implementation and Heuristic Function
In practical scenarios, such as autonomous driving, Q-Star's policy guides decision-making through a heuristic function that balances accumulated utility (g) and heuristic estimates (h) of future states. This heuristic function is central to Q-Star, providing a dynamic mechanism to evaluate and select actions based on both immediate outcomes and anticipated future rewards. The iterative optimization of these decisions facilitates an increasingly refined reasoning process, which is crucial for applications requiring high reliability and precision.
Performance Evaluation and Comparative Analysis
The efficacy of Q-Star is highlighted through performance comparisons with conventional models like GPT-3.5 and newer iterations such as GPT Turbo and GPT-4. The document details a benchmarking study where Q-Star outperforms these models by implementing a refined heuristic search strategy that maximizes utility functions. This superior performance underscores Q-Star’s potential to significantly enhance LLM's reasoning capabilities, particularly in complex, multi-step scenarios where traditional models falter.
Future Directions and Concluding Insights
The document concludes with a discussion on the future trajectory of Q-Star and multi-step reasoning optimization. The insights suggest that while Q-Star represents a considerable advancement in LLM reasoning, the complexity of its implementation and the computational overhead involved pose substantial challenges. Further research is encouraged to streamline Q-Star's integration across various AI applications and to explore new heuristic functions that could further optimize reasoning processes. The ultimate goal is to develop a universally applicable framework that not only enhances reasoning accuracy but also reduces the computational burden, making advanced AI reasoning more accessible and efficient.
All rights w/ authors:
Q*: Improving Multi-step Reasoning for LLMs with
Deliberative Planning
arxiv.org/pdf/2406.14283
#airesearch
#ai
#scienceandtechnology 
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firehose to my brain. Amazing! This indicates a fairly long path of steps I need to learn so I can properly digest this beautiful wisdom. Really amazing channel, filled with advanced knowledge of the gods.
This is very good introduction and enjoy the contents even I have implemented my Q-Learning by myself around 30 years ago.
Interesting explanation! You mentioned there is code to try it yourself, but I cannot find that. Can you point me to it?
please tag me also once you find the code
Your presentation in this video is definitely A+ in terms of clarity and depth of understanding! well done. Also, I am happy to see a real paper and study on the speculative Q* heuristic search algorithm. Although their results seems to not justify the effort and added complexity, we are only looking at well-known math problems that those LLMs might be pre-trained and focused a lot. If we change the angle to the algorithm is applied in the general solution search space, with greater complexity, Q* is the way to go!
Amazing video as always
I thought Q* was supposed to be a project by Google or OpenAI (I forget which, but I thought it was supposed to be one of them).
The authors listed in the paper are indicated as being affiliated with either “Skywork AI” or “Nanyang Technology university”?
Is this a model inspired by the rumors of there being a model with the name “Q*”, or is this the model the rumors were about? Were some of these people previously at OpenAI or Google, but not anymore? Or..?
It was OpenAI internal document leaks I believe. I’m wondering the same thing! I feel like it has to be related, otherwise this feels kind of wrong. I understand wanting to get eyes on your research, and this seems like good research so I commend them on that, but still. If anyone has more info, leave a reply.
I'm also really confused. Skywork AI seems to be a legit company/research group, they have released models in the past. however, I see no indication that their Q* is related to OpenAI's.
the authors of this paper don't seem to have a record on big tech companies.
one of the authors, Chaojie Wang, has a github page which gives some more context (you can look it up on Google if you want)
I also was quite confused! It doesn't seem like the people behind the paper have any relation with big tech companies (Google, OpenAI, Microsoft, etc.) and it doesn't seem like their paper is directly related to OpenAI's supposed Q*
my old comment got deleted, perhaps bc some word triggered the algorithm. I just said you can use search to find out more about the authors, the first one in the cover of the paper immediately answers many questions.
The trick is whether you have a Q* over a sequence or if you figured out how to credit a sequence for good or bad. "The Credit assignment problem". Possibly OpenAI has figured out a fine grained Q* which would give fast accurate feedback and learning.
Thank you for such a detailed explanation. Really enjoyed it 🚀.
But is this Q* somewhat relevant to the one from OpenAI that people have been talking about 🧐?
Yay! 😎🤖
Small tip: black borders on white font makes text easier to read and less tiring to watch
27:58 : you say “estimated utility of reaching the correct answer”. Does this mean “an estimate of what the utility would be if the correct answer is obtained” (which sounds to me like the plainest interpretation , but also the least likely, as I would think the utility for that would be arbitrary) or “the expected value of the random variable which gives utility based just on whether final answer is correct”, or “the expected value of the random variable, utility, which is determined by both whether the final answer is correct, and other things, such as length of answer”, or something else?
wow
First viewer here 🗡️
246th view. Nailed it!
LoL. Third I guess. Well Yacinezahidi was 0th user, is 1st, and I'm 2nd.
Fourth then 😂
Second