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
Launch
CandyDrop
Collect candies to earn airdrops
Launchpool
Quick staking, earn potential new tokens
HODLer Airdrop
Hold GT and get massive airdrops for free
Pre-IPOs
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
Promotions
AI
Gate AI
Your all-in-one conversational AI partner
Gate AI Bot
Use Gate AI directly in your social App
GateClaw
Gate Blue Lobster, ready to go
Gate for AI Agent
AI infrastructure, Gate MCP, Skills, and CLI
Gate Skills Hub
10K+ Skills
From office tasks to trading, the all-in-one skill hub makes AI even more useful.
GateRouter
Smartly choose from 40+ AI models, with 0% extra fees
ByteDance open-sources Cola DLM: Redefining text generation with diffusion models
This is a set of continuous latent diffusion language models that attempt to bypass the fixed token-by-token generation path of large language models, changing text generation to first organize high-level semantics and then revert to specific words.
The core of Cola DLM is Text VAE + block-causal DiT.
Text VAE first maps discrete text into a continuous latent space, and block-causal DiT then learns the latent prior through Flow Matching.
Finally, a conditional decoder restores the latent variables back into text.
The diffusion process handles latent semantic representations, not repeatedly denoising directly at the token level.
This open-source version is a 2B-level model, with approximately 2.3 billion total parameters, including a core DiT with 1.8 billion parameters and an additional 500 million parameters for VAE.
In evaluations such as LAMBADA, MMLU, OBQA, HellaSwag, RACE, SIQA, SQuAD, and Story Cloze, the paper states that under a unified generative evaluation protocol, it has demonstrated scaling performance competitive with baseline models of the same size like AR / LLaDA, and achieved the best results in the final average score.
However, it is currently still a research checkpoint, not a directly usable dialogue model.
The official note states that this model has not undergone instruction fine-tuning or RLHF, and its main purpose is to study how continuous latent diffusion can be used for text generation.
The paper also shows preliminary experiments extending to unified modeling of text and images, but this open-source repository only includes the text pipeline.
(Source: BlockBeats)