Buy Crypto
Pay with
USD
USD
Buy & Sell
Hot
Supports Visa, Mastercard, SEPA & more
P2P
0 Fees
Flexible trading, zero fees
Gate Card
Use your crypto for payments worldwide
Markets
Trade
Basic
Advanced
Futures
Futures
Access hundreds of perpetual contracts
CFD
Gold
One platform for global traditional assets
Options
Hot
Trade European-style vanilla options
Unified Account
Maximize your capital efficiency
Demo Trading
Introduction to Futures Trading
Learn the basics of futures trading
Futures Events
Join events to earn rewards
Demo Trading
Use virtual funds to practice risk-free trading
Stocks
CFD
U.S. stock CFD derivatives
US Stocks
Access real US stocks and ETFs
HK Stocks
Trade quality Hong Kong-listed stocks
Korean Stocks
SK Hynix
Real Korean stocks and top assets
Stock Futures
High leverage, 24/7 trading
Tokenized Stocks
Backed by real stock assets
IPO Access
Unlock full access to global stock IPOs
GUSD
Mint GUSD for Treasury RWA yields
Stocks Activities
Trade Popular Stocks and Unlock Generous Airdrops
Earn
Launch
CandyDrop
Collect candies to earn airdrops
Launchpool
Quick staking, earn potential new tokens
HODLer Airdrop
Hold GT and get massive airdrops for free
IPO Access
Unlock full access to global stock IPOs
Alpha Points
Trade on-chain assets and earn airdrops
Futures Points
Earn futures points and claim airdrop rewards
Investment
Square
Square
Post, share, and explore crypto trends
Live
moments live
Live crypto market analysis
Chat
Chat with crypto traders
News
What is happening in crypto
flower_head
More
Promotions
AI
Others
TradFi
2026 World Cup
Rewards
Square
Latest
Hot
News
Profile

Luo Fuli: Large models are entering the post-training era, with top teams achieving a 1:1 ratio of pre-training to post-training compute.

MeNews
robot
Abstract generation in progress
ME News, April 24 (UTC+8), according to Beating monitoring, Luo Fuli, head of Xiaomi's large model team, pointed out that the competition of large models has fully shifted from the pre-training-dominated Chat era to the post-train-dominated Agent era. The core race now is "how to effectively scale reinforcement learning (RL) on agents." This paradigm shift directly leads to a reconfiguration of computing power allocation. Luo Fuli revealed that in the Chat era, the computing power ratio for research, pre-training, and post-training was approximately 3:5:1; in today's Agent era, the reasonable allocation ratio has become 3:1:1, meaning the computing power investment in pre-training and post-training is basically equal. Currently, top model teams have already reached a 1:1 ratio in these two areas. Meanwhile, system architecture requirements have also undergone dramatic changes. In the past, RL infrastructure was mainly centered on "model inference engines" handling pure text computation; now, the infrastructure must be centered on "agent" to support heterogeneous cluster scheduling and tolerate the ambiguity of agents being interrupted by various uncontrollable factors in complex workflows. (Source: BlockBeats)
View Original
This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.
  • Reward
  • Comment
  • Repost
  • Share
Comment
Add a comment
Add a comment
No comments

  • Pinned

Sitemap