Qwen released a game changer AI is evolving fast, and two models are making waves: QwQ-32B and DeepSeek-R1. You’d think bigger means better, but QwQ is proving that’s not always the case. DeepSeek-R1 boasts 671 billion parameters (though only 37B are active), making it a giant in AI. But QwQ-32B, with just 32 billion parameters, achieves comparable performance—and that’s a game-changer. So what’s the secret? Reinforcement Learning (RL). Instead of just memorizing data, QwQ learns from feedback—just like how we improve through trial and error. RL rewards better responses, helping the model refine its reasoning and decision-making over time. This is huge for AI efficiency. Smaller models that perform as well as larger ones mean lower costs, faster response times, and more accessibility. Imagine getting ChatGPT-4 level performance without needing a supercomputer! This also raises big questions: • Will AI move toward leaner, smarter models? • Is reinforcement learning the future of LLMs? • What does this mean for Google, OpenAI, Meta, and others? One thing’s clear—AI isn’t slowing down anytime soon. Smarter, not bigger, might be the next frontier. What do you think? Are we heading for an AI efficiency revolution? Let me know in the comments! 👇 #product #productmanager #productmanagement #startup #business #openai #llm #ai #microsoft #google #gemini #anthropic #claude #llama #meta #nvidia #career #careeradvice #mentor #mentorship #mentortiktok #mentortok #careertok #job #jobadvice #future #2024 #story #news #dev #coding #code #engineering #engineer #coder #sales #cs #marketing #agent #work #workflow #smart #thinking #strategy #cool #real #jobtips #hack #hacks #tip #tips #tech #techtok #techtiktok #openaidevday #aiupdates #techtrends #voiceAI #developerlife #qwen #deepseek #cursor #replit #pythagora #bolt

You know how long it took for DeepSeek to get beat? Two months. So Quen released QWQ32B, which is a 32 billion parameter model, which sounds very big and it's tiny. This was developed by the company Alibaba, which runs the site you're thinking of. And even though it's a really small model compared to the 670 some billion parameter model that DeepSeek has built, it still performs about the same way on reasoning, on mathematics, etc. You might be asking why? Totally reasonable question. Well, it turns out that they are using really heavy reinforcement learning techniques that enable it to solve problems based on feedback. And so what that looks like is, imagine that the agent is learning and training what works and what doesn't, what humans want, what humans don't. The agent observes something. The agent chooses an action. We call this a policy in reinforcement learning. The environment comes back with a reward that's either positive or negative. Either yay, great job, or nope, wrong choice. The agent then updates the policy based on the reward and the cycle repeats. So everybody uses reinforcement learning. Quen used it a whole lot to get the results they got. Typically what that means is that it is going to be a more brittle model. So what I find, and this is often also true of smaller models, so usually smaller models and models that have heavier reinforcement learning are really, really good across the scenarios that they have had reinforcement learning work for, but they may be somewhat unpredictable if you are doing questions, challenges that don't fit into that reinforcement learning experience. Because they, as a smaller model, just aren't as predictable fundamentally. And so reinforcement learning acts to rein that in across high-value tasks, but that's not always what you get if you go off the reservation, so to speak. So Quen is amazing, and I don't say any of that to sort of downplay the experience and sort of what it feels like to use the model or the value of producing a small model that performs this well. Those are all incredible achievements. They've open-sourced it. It's a remarkable thing. But I don't want you to hear that it's a remarkable model and it's on par with DeepSeq and think it's the best of the best right now, because the best of the best remain models that have larger parameter size and that also include reasoning. An example would be Clod 3.7 is really strong right now. O3 is incredible right now. So at this point, we're talking about models that are all very good. Models that we would all have been blown away by in 2023 or 2024. And it's just about figuring out which model works for you in a given situation. And people will use Quen in situations where they want a model that's effective and they aren't ready to put a model that's larger into place. And so this can be really, really good in production environments where memory matters and perhaps speed matters, and you're going to get similar kinds of queries that Quen will handle really effectively. So we will see, but Quen producing this model so quickly and it competing with DeepSeq is a big deal. It's a model that's about 20 times smaller. So let me know what you think if you've played with it.