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Joe
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Joe
@joe_fill1
Developer @inoxxprotocol | Former Backend @ DeFiLlama | Coded @clcoding | ex-contributor at @Fetch_ai | Blockchain, AI & Trading Enthusiast | TA & charting
World Katılım Mayıs 2023
774 Takip Edilen5.2K Takipçiler
Joe retweetledi
HAPPY CRYPTO WEEK! The House will soon VOTE on a tremendous Bill to Make America the UNDISPUTED, NUMBER ONE LEADER in Digital Assets - Nobody does it better! The GENIUS Act is going to put our Great Nation lightyears ahead of China, Europe, and all others, who are trying endlessly to catch up, but they just can’t do it. Digital Assets are the FUTURE, and we are leading by a lot! Get the first Vote done this afternoon (ALL REPUBLICANS SHOULD VOTE YES!). This is our moment - Digital Assets, GENIUS, Clarity! It is all part of Making America Great Again, BIGGER AND BETTER THAN EVER BEFORE. We are leading the World, and will work hard with the Senate and the House to get even more Legislation on this passed! (TS: 15 Jul 15:29 UTC)
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@scottmelker Pretty rich coming from someone who is been in charge during actual financial ruin.
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MAXINE WATERS SAYS GENIUS ACT AND CLARITY ACT COULD "OPEN THE FLOODGATES TO MASSIVE FRAUD AND FINANCIAL RUIN MILLIONS OF AMERICAN FAMILIES."
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Joe retweetledi
JUST IN: Standard Chartered becomes first global bank to offer institutional Bitcoin and Ethereum trading.
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@cz_binance Imagine turning $100 into $1M… and still holding
Happy birthday
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Scaling up RL is all the rage right now, I had a chat with a friend about it yesterday. I'm fairly certain RL will continue to yield more intermediate gains, but I also don't expect it to be the full story. RL is basically "hey this happened to go well (/poorly), let me slightly increase (/decrease) the probability of every action I took for the future". You get a lot more leverage from verifier functions than explicit supervision, this is great. But first, it looks suspicious asymptotically - once the tasks grow to be minutes/hours of interaction long, you're really going to do all that work just to learn a single scalar outcome at the very end, to directly weight the gradient? Beyond asymptotics and second, this doesn't feel like the human mechanism of improvement for majority of intelligence tasks. There's significantly more bits of supervision we extract per rollout via a review/reflect stage along the lines of "what went well? what didn't go so well? what should I try next time?" etc. and the lessons from this stage feel explicit, like a new string to be added to the system prompt for the future, optionally to be distilled into weights (/intuition) later a bit like sleep. In English, we say something becomes "second nature" via this process, and we're missing learning paradigms like this. The new Memory feature is maybe a primordial version of this in ChatGPT, though it is only used for customization not problem solving. Notice that there is no equivalent of this for e.g. Atari RL because there are no LLMs and no in-context learning in those domains.
Example algorithm: given a task, do a few rollouts, stuff them all into one context window (along with the reward in each case), use a meta-prompt to review/reflect on what went well or not to obtain string "lesson", to be added to system prompt (or more generally modify the current lessons database). Many blanks to fill in, many tweaks possible, not obvious.
Example of lesson: we know LLMs can't super easily see letters due to tokenization and can't super easily count inside the residual stream, hence 'r' in 'strawberry' being famously difficult. Claude system prompt had a "quick fix" patch - a string was added along the lines of "If the user asks you to count letters, first separate them by commas and increment an explicit counter each time and do the task like that". This string is the "lesson", explicitly instructing the model how to complete the counting task, except the question is how this might fall out from agentic practice, instead of it being hard-coded by an engineer, how can this be generalized, and how lessons can be distilled over time to not bloat context windows indefinitely.
TLDR: RL will lead to more gains because when done well, it is a lot more leveraged, bitter-lesson-pilled, and superior to SFT. It doesn't feel like the full story, especially as rollout lengths continue to expand. There are more S curves to find beyond, possibly specific to LLMs and without analogues in game/robotics-like environments, which is exciting.
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Will Bitcoin hit $200,000 this cycle?
Like = Yes ❤️
Retweet = No 🔄
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99.99% of bitcoin buyers and holders are in profit.
Tell me about your cabal / meme coin performance my guy?
@_ck007 @ezz_ai @GoingParabolic
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