Offshore
Photo
memenodes
If it's not for women most guys will live like this and see nothing wrong. https://t.co/wzmPJbke0k
tweet
If it's not for women most guys will live like this and see nothing wrong. https://t.co/wzmPJbke0k
tweet
Offshore
Photo
God of Prompt
RT @godofprompt: 🚨 MIT proved you can delete 90% of a neural network without losing accuracy.
Five years later, nobody implements it.
"The Lottery Ticket Hypothesis" just went from academic curiosity to production necessity, and it's about to 10x your inference costs.
Here's what changed (and why this matters now):
tweet
RT @godofprompt: 🚨 MIT proved you can delete 90% of a neural network without losing accuracy.
Five years later, nobody implements it.
"The Lottery Ticket Hypothesis" just went from academic curiosity to production necessity, and it's about to 10x your inference costs.
Here's what changed (and why this matters now):
tweet
Offshore
Photo
memenodes
Someone threw his girl away…what will you do in this situation?? https://t.co/ChwwmV486c
tweet
Someone threw his girl away…what will you do in this situation?? https://t.co/ChwwmV486c
tweet
Offshore
Photo
memenodes
“I bet he’s texting other women”
She doesn’t know I’m monitoring the USA-Venezuela war situation https://t.co/uYCQH41KOt
tweet
“I bet he’s texting other women”
She doesn’t know I’m monitoring the USA-Venezuela war situation https://t.co/uYCQH41KOt
tweet
Offshore
Photo
God of Prompt
RT @rryssf_: 🚨 This paper quietly explains why most AI agents feel impressive… until you put them to real work.
Most agents fail not because models are weak, but because we train them like chatbots, not workers.
Here’s the core problem the paper exposes:
Today’s agents are brittle.
They rely on hand-written workflows, static prompts, and frozen policies.
They don’t adapt when tasks change, tools break, or environments get messy.
Youtu-Agent flips this by treating agents like evolving systems, not fixed scripts.
The system has two key ideas.
First: Automated agent generation at scale.
Instead of manually designing one “smart” agent, Youtu-Agent automatically generates many candidate agents with different behaviors, tool strategies, and task decompositions. Think of it as population-based agent design.
Second: Hybrid policy optimization.
This is the real breakthrough.
Rather than relying only on supervised learning or pure reinforcement learning, Youtu-Agent combines:
• imitation from strong demonstrations
• reinforcement learning from task-level rewards
• online self-improvement during execution
The agent doesn’t just learn what to say. It learns how to act better over time.
One result that stood out: agents trained this way complete complex multi-step tasks with significantly fewer failures, fewer redundant actions, and higher end-to-end success rates across benchmarks.
Most “AI agents” today are demos.
They look good on short tasks but collapse under long horizons.
Youtu-Agent shows that productivity scales when agents can:
• generate their own strategies
• evaluate themselves
• update policies continuously
• balance imitation with exploration
This is not prompt engineering.
This is agent engineering.
If you’re building assistants, copilots, or autonomous systems, this paper is a warning shot:
Static agents are dead on arrival.
The future belongs to agents that generate, optimize, and evolve their own behavior.
Read the full paper here: https://t.co/IeLVhb7l91
tweet
RT @rryssf_: 🚨 This paper quietly explains why most AI agents feel impressive… until you put them to real work.
Most agents fail not because models are weak, but because we train them like chatbots, not workers.
Here’s the core problem the paper exposes:
Today’s agents are brittle.
They rely on hand-written workflows, static prompts, and frozen policies.
They don’t adapt when tasks change, tools break, or environments get messy.
Youtu-Agent flips this by treating agents like evolving systems, not fixed scripts.
The system has two key ideas.
First: Automated agent generation at scale.
Instead of manually designing one “smart” agent, Youtu-Agent automatically generates many candidate agents with different behaviors, tool strategies, and task decompositions. Think of it as population-based agent design.
Second: Hybrid policy optimization.
This is the real breakthrough.
Rather than relying only on supervised learning or pure reinforcement learning, Youtu-Agent combines:
• imitation from strong demonstrations
• reinforcement learning from task-level rewards
• online self-improvement during execution
The agent doesn’t just learn what to say. It learns how to act better over time.
One result that stood out: agents trained this way complete complex multi-step tasks with significantly fewer failures, fewer redundant actions, and higher end-to-end success rates across benchmarks.
Most “AI agents” today are demos.
They look good on short tasks but collapse under long horizons.
Youtu-Agent shows that productivity scales when agents can:
• generate their own strategies
• evaluate themselves
• update policies continuously
• balance imitation with exploration
This is not prompt engineering.
This is agent engineering.
If you’re building assistants, copilots, or autonomous systems, this paper is a warning shot:
Static agents are dead on arrival.
The future belongs to agents that generate, optimize, and evolve their own behavior.
Read the full paper here: https://t.co/IeLVhb7l91
tweet