📜 A new blog post explaining the fundamentals of RL for LLMs—the perfect place to intuitively understand PPO and RLHF from first principles. 👇👇 Pre-trained LLMs are often compared to Lovecraft's Shoggoth, a monstrous, shape-shifting beast. PPO is the algorithm that brought it under control. Take a look! 🔗 https://lnkd.in/eXqDvrUV
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Timed reading of closed sets of high frequency words paired with priming exercises between trials...best instructional routine I've found to structure HFW recognition in a Deliberate Practice framework. When is it critical to help struggling readers automatize HFW recognition? Certainly if already off the ORF standard for passage reading:
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How effectively can we align base LLMs without SFT or RLHF? URIAL achieves alignment through in-context learning of base LLMs and three stylistic examples and a system prompt. ⚖️ SFT Mistral-7b / RLHF Llama-2-70b Code https://buff.ly/3Uij52u Paper https://buff.ly/3A7RdqU
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Mrs Preece's Y13s have been creating glucose calibration curves as part of their required practical, using a colorimeter. Some pretty qualitative results as well as collecting quantitative result to plot their calibration curve next lesson. The Arthur Terry Learning Partnership (ATLP)
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The discussion this qu sparked with my Y11s today was interesting: “Is 1 a square number?” “What about 25 - can I include that?” Revisiting basic facts and exam technique often uncovers surprising gaps in understanding. https://lnkd.in/e9HAdwh9 #maths #mathschat #MathsToday
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ML Consultant @ Vesuvius Challenge | PhD in AI | Physicist | Data Scientist
4moSir, nice blog post. I have a silly question. RL is a framework to obtain a (sub-)optimal policy for a sequential decision making problem under uncertainty. Usually, after the agent takes an action, there is an environment that changes the state of the system in a possibly stochastic way. In what you are describing, the next state of the system is dictated deterministically by the action (the next token) produced by the agent (the LLM). Therefore, wouldn't the problem be better formalized as a multi-armed bandit? It would make more sense to use the general RL formalism if you were to train on full conversations rather than just prompt-answers, in that case the "environment" could be the human user that changes the state in a stochastic way with his prompts. Does this make sense to you?