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No one plays [Devils Never Cry] better than [One-Man Show] 🔴Post goal: 150 retweets 🎁Reward: 5 Random Hunters get a $10 gift card each 🌎Pre-register: #DevilMayCryPeakofCombat #Capcom #DevilMayCry #RockTheBloodyHunt
374,309 次观看 • 2 年前 •via X (Twitter)
9 条评论
Devil May Cry: Peak Of Combat2 年前
Congratulations winners! @nexamusfortress @revvy_afterlife @hopeis_my @Lonewol64 @TheDaikuri Thank you for participating in the event! Please check your DMs to see the reward!
Nikkisparda2 年前
Ya quiero jugarlo oooo T u T
Mister_P2 年前
@JerichoFive
leo2 年前
🥱
Leave3D 🌌 / 残す3だ (also on other app)2 年前
"But you are never become a man"
Rykard2 年前
that's a shit reward. Give me 10 s rank weapons
Rahul Vaghela2 年前
When it will be available in India?
ANDROOSGAMEPLAY2 年前
I'm still getting an error with the gift card I won😂
Andres2 年前
Yo quiero mi regalo de hunters y la gift de 10 por favor yo vengo aportando críticas constructivas y chequeando las fallas y optimización del juego para después decirles a ustedes que deben mejorar soy participante de las betas anteriores deseo ganar
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![◤#JujutsuKaisenPhantomParade◢ ＼Theme Song Released!／ The theme song "Avant" for [#JujutsuKaisenPhantomParade] has officially been released! Youtube: Additionally, we've prepared some rewards for everyone; participating in the event gives you a chance to win! ▼ How to Participate: Follow the official account and share your thoughts on the theme song in this post to participate in the event. ▼ Event Period: Event Participation Period: 2024/10/31 - 2024/11/7 23:59 (UTC +9) List of Winners: 2024/11/8 Player Information Collection Period: 2024/11/9 - 2024/11/16 23:59 (UTC +9) ▼ Event Rewards: We will randomly select one player from those who correctly participate in the event to receive an iPhone 16, and we will also choose 10 lucky players to each receive a $10 gift card. Join the event now! ▼#JujutsuKaisenPhantomParade is now available for pre-registration▼ [Pre-register on App Store]: [Pre-register on Google Play]: [Official website]: [SNS]: #JujutsuKaisenPhantomParade #JujutsuKaisenOfficialMobileGame #jjkphantomparade #JujutsuKaisen #JJKPPOST](https://image.24vids.com/tw-1851837976990617688/ext_tw_video_thumb/1851809675781771264/pu/img/l9JwHE1NrMQzVDgr.jpg)
![✨Infinity Nikki Reunion Playtest Trailer [\\\Nikki Nikki///] Is Coming! ♾Follow Infinity Nikki and repost this post for a chance to be one of 20 lucky stylists selected at random to win PS5*1, cash, or game merchandise! 🎁Prizes: Grand Prize: A PS5 console Disc Edition Slim (1 winner) Cash Prize: $50 (5 winners, $50 each) Game Merchandise: Infinity Nikki Merch (14 winners) (Includes Infinity Nikki Whimstar Cushion*1） ♾Event Duration: Aug. 20 to Sep. 2 Submission Review: Sep. 3 to Sep. 10 Result Announcement: Sep. 11 to Sep. 18 Infinity Nikki [Reunion Playtest] Recruitment is now open!💫 👀New landscapes, adventures, and friends are waiting for you! 🚂Dear Stylists, hop aboard this whimsical train and immerse yourselves in the coziest open world! >>>Pre-Register Now: >>>Join us on Discord: #InfinityNikki #InfinityNikkiWhimTour #Gamescom2024](https://image.24vids.com/tw-1826077070121906558/ext_tw_video_thumb/1826064285182615552/pu/img/YLSuuBbX2fAwbnh6.jpg)
![RLHF by hand ✍️ ~ 15 steps walkthrough below Train a model on human text and it inherits human bias. It will assume a doctor is a "him", because the data says so. RLHF is the correction. A human marks one preference, doc is them over doc is him, and the weights move. But one correction is not the point. The hope is that the model learns the value behind it, gender neutrality, and applies it to professions nobody ever mentioned. How does it work? Goal: train a reward model from a single human comparison about doctors, then turn it on CEOs, filling in every cell yourself. = 1. Given = A reward model, an LLM, and two (prompt, next) pairs. = 2. Preferences = A human reads both pairs and picks a winner: (doc is, them) beats (doc is, him). The loser is not bad grammar, it is gender bias, and that is the whole signal. = 3. Word embeddings = Let us look up each word of the loser pair. These vectors are the reward model's input. = 4. Linear layer = We multiply by the reward model's weights and add its biases. Out come feature vectors, one per position. = 5. Mean pool = Let us multiply by [1/3, 1/3, 1/3], which averages the three positions into one sentence embedding. = 6. Output layer = We map that sentence down to a single number. Reward = 3. = 7. The winner, the same way = Let us repeat steps 3 to 6 on the winning pair. Reward = 5. = 8. Winner minus loser = We take the gap: 5 - 3 = 2. The reward model wants this positive and as large as it can make it. = 9. Loss gradient = Let us squash the gap into a probability, σ(2) ≈ 0.9, and subtract the target of 1. The gradient is -0.1, and it goes back through the purple weights. The reward model is now trained. = 10. A prompt it has never seen = We start the second half with "[S] CEO is". The feedback in step 2 was about doctors. Nothing connects a CEO to a doctor except what the reward model generalised. = 11. Transformer = Let us push it through attention and a feed forward layer, one vector per position. = 12. Output probabilities = We map each vector to a score over the vocabulary. = 13. Sample = Let us take the highest score. The model completes "CEO is" with "him", which is the same bias the human penalised in step 2. = 14. Score it with the reward model = We feed the new pair (CEO is, him) through steps 3 to 6. Reward = 3, exactly the score it gave "doc is him" in step 6. Nobody taught it about CEOs. The value transferred. = 15. Loss gradient = Let us set the loss to the negative of the reward, so minimising the loss maximises the reward. The gradient is a constant -1, and it goes back through the red weights. The outputs: Loser reward = 3, winner reward = 5 Reward gap = 2, predicted σ ≈ 0.9, reward model gradient = -0.1 LLM samples "him", reward = 3, LLM gradient = -1 Congrats! You just calculated RLHF by hand. And you watched a value generalise: one comparison about doctors, and the model marks down "CEO is him" unprompted. 💾 Save this post!](https://image.24vids.com/tw-2093341659677196448/ext_tw_video_thumb/2093341615968473088/pu/img/KZ0x3r9oKjH8za-m.jpg)
![RLHF by hand ✍️ ~ 15 steps walkthrough below Train a model on human text and it inherits human bias. It will assume a doctor is a "him", because the data says so. RLHF is the correction. A human marks one preference, doc is them over doc is him, and the weights move. But one correction is not the point. The hope is that the model learns the value behind it, gender neutrality, and applies it to professions nobody ever mentioned. How does it work? Goal: train a reward model from a single human comparison about doctors, then turn it on CEOs, filling in every cell yourself. = 1. Given = A reward model, an LLM, and two (prompt, next) pairs. = 2. Preferences = A human reads both pairs and picks a winner: (doc is, them) beats (doc is, him). The loser is not bad grammar, it is gender bias, and that is the whole signal. = 3. Word embeddings = Let us look up each word of the loser pair. These vectors are the reward model's input. = 4. Linear layer = We multiply by the reward model's weights and add its biases. Out come feature vectors, one per position. = 5. Mean pool = Let us multiply by [1/3, 1/3, 1/3], which averages the three positions into one sentence embedding. = 6. Output layer = We map that sentence down to a single number. Reward = 3. = 7. The winner, the same way = Let us repeat steps 3 to 6 on the winning pair. Reward = 5. = 8. Winner minus loser = We take the gap: 5 - 3 = 2. The reward model wants this positive and as large as it can make it. = 9. Loss gradient = Let us squash the gap into a probability, σ(2) ≈ 0.9, and subtract the target of 1. The gradient is -0.1, and it goes back through the purple weights. The reward model is now trained. = 10. A prompt it has never seen = We start the second half with "[S] CEO is". The feedback in step 2 was about doctors. Nothing connects a CEO to a doctor except what the reward model generalised. = 11. Transformer = Let us push it through attention and a feed forward layer, one vector per position. = 12. Output probabilities = We map each vector to a score over the vocabulary. = 13. Sample = Let us take the highest score. The model completes "CEO is" with "him", which is the same bias the human penalised in step 2. = 14. Score it with the reward model = We feed the new pair (CEO is, him) through steps 3 to 6. Reward = 3, exactly the score it gave "doc is him" in step 6. Nobody taught it about CEOs. The value transferred. = 15. Loss gradient = Let us set the loss to the negative of the reward, so minimising the loss maximises the reward. The gradient is a constant -1, and it goes back through the red weights. The outputs: Loser reward = 3, winner reward = 5 Reward gap = 2, predicted σ ≈ 0.9, reward model gradient = -0.1 LLM samples "him", reward = 3, LLM gradient = -1 Congrats! You just calculated RLHF by hand. And you watched a value generalise: one comparison about doctors, and the model marks down "CEO is him" unprompted. 💾 Save this post!](https://image.24vids.com/tw-2081745292051845420/ext_tw_video_thumb/2081745247344676864/pu/img/ahcUqHG6T2iBoOlI.jpg)
![[RLHF] by Hand ✍️ Yesterday, Jan Leike (Jan Leike) announced he is joining #Anthropic to lead their "super-alignment" mission. He is the co-inventor of Reinforcement Learning with Human Feedback (#RLHF). How does RLHF work? [1] Given ↳ Reward Model (RM) ↳ Large Language Model (LLM) ↳ Two (Prompt, Next) Pairs 🟪 TRAIN RM Goal: Learn to give higher rewards to winners [2] Preferences ↳ A human reviews the two pairs and picks a "winner" ↳ (doc is, him) Embeddings ↳ This prompt has never received human feedback directly ↳ [S] is the special start symbol [11] Transformer ↳ Attention (yellow) ↳ Feed Forward (4x2 weight and bias matrix) ↳ Output: 3 "transformed" feature vector, one per position ↳ More details in my previous post 8. Transformer [] [12] Output Probabilities ↳ Apply a linear layer to map each transformed feature vector to a probability distribution over the vocabulary. [13] Sample ↳ Apply the greedy method, which is to pick the word with the highest score ↳ For output 1 and 2, the model accurately predicts the next word ↳ For 3rd output position, the model's predicts "him" [14] Reward Model ↳ The new pair (CEO is, him) is fed to the reward model ↳ The process is same as [3]-[6] ↳ Output: Reward = 3 [15] Loss Gradient ↳ We set the loss as the negative of the reward. ↳ The loss gradient is simply a constant -1. ↳ Run backpropagation and gradient descent to update LLM's weights and biases (red border)](https://image.24vids.com/tw-1795803665845899511/ext_tw_video_thumb/1795803020149534720/pu/img/jb8SBLT_1drHtYd_.jpg)
![Discrete Fourier Transform by hand ✍️ ~ 12 steps walkthrough below Here is a little-known secret about the DFT and the inverse DFT: it is just matrix multiplication in both directions, one the transpose of the other, exactly like the forward pass and backpropagation I drew in other examples. Goal: recover which cosine waves a signal is made of, using nothing but multiplication and addition. = 1. Given = Three signals written as sums of cosines, and a fourth, X, that we do not know yet. = 2. Frequency matrix F = Let us write the coefficients as a matrix. Each signal is a row, each frequency a column, so A = cos(w) + 2cos(2w) becomes [1, 2, 0, 0]. = 3. Sample the waves = We read the four cosine waves at ten discrete time points. That word "discrete" is the whole difference between this and the continuous transform. = 4. Cosine matrix W = Let us write those samples as a matrix: each frequency a row, each time point a column. = 5. Frequency to time = We multiply F by W. That combines the four cosine waves in the proportions F specifies, and the result T is the three signals as they would look in time. = 6. Transpose = Let us stand each signal up as a column. = 7. Time to frequency = We multiply W by that transpose. Every cell is the dot product of one signal with one cosine wave, which measures how much of that wave the signal contains. Zero means none of it. = 8. Scale = Let us multiply by 2/n, with n = 10. The projections come out five times too large, and this is the correction. = 9. Transpose back = We turn it back around, and it is F again, exactly. That is the check: the transform recovered the coefficients we started from. = 10. Now solve for X = Let us run the same multiplication on the one signal whose recipe we never knew. = 11. Scale = We divide by 5 again. = 12. Transpose back = And X reads [0, 0, 3, 2], which says X = 3cos(3w) + 2cos(4w). Note: I originally drew this to show that the DFT is a special case of a convolution layer, its filters fixed to sine and cosine waves rather than learned. No wonder, then, that a convolution layer free to learn its own filters can be trained to process signals. 💾 Save this post!](https://image.24vids.com/tw-2083194764485153128/ext_tw_video_thumb/2083194751193403393/pu/img/xPTCxxe0pHHx3B7I.jpg)