Maincode/Maincoder-1B
Text Generation
ā¢
1B
ā¢
Updated
ā¢
679
ā¢
87
Sarthak Thakur
sarthak-maincode
AI & ML interests
None yet
Recent Activity
updated
a model
26 days ago
Maincode/Maincoder-1B
new activity
26 days ago
Maincode/Maincoder-1B:Model eval
new activity
about 1 month ago
Maincode/Maincoder-1B:When a GGUF would be available?
Organizations
updated
a
model
26 days ago
Model eval
5
#2 opened about 1 month ago
by
kth8
When a GGUF would be available?
15
#1 opened about 1 month ago
by
FlameF0X
Hi
@FlameF0X
, I was going through your HF and your Github and I know you don't wish to be contacted but just in case you change your mind I'd drop my Linkedin here (would have dropped my email too but I just don't wish to be spammed from other people xD). Let's see where things go from here.
Looking forward to hearing from your side :)
liked
a
model
about 1 month ago
Maincode/Maincoder-1B
š
š„
9
2
#7039 opened about 1 month ago
by
sarthak-maincode
upvoted
a
paper
4 months ago
liked
2
models
4 months ago
reacted to
m-ric's
post with š
4 months ago
Post
4937
STOP EVERYTHING NOW - we might finally have a radical architecture improvement over Transformers!!! šØ
A lone scientist just proposed Tiny Recursive Model (TRM), and it is literally the most impressive model that I've seen this year.
ā”ļø Tiny Recursive Model is 7M parameters
ā”ļø On ARC-AGI, it beats flagship models like Gemini-2.5-pro
Consider how wild this is: Gemini-2.5-pro must be over 10,000x bigger
and had 1,000 as many authors š (Alexia is alone on the paper)
What's this sorcery?
In short: it's a very tiny Transformers, but it loops over itself at two different frequencies, updating two latent variables: one for the proposed answer and one for the reasoning.
@AlexiaJM started from the paper Hierarchical Reasoning Model, published a few months ago, that already showed breakthrough improvement on AGI for its small size (27M)
Hierarchical Reasoning Model had introduced one main feature:
š Deep supervision
In their model, one part (here one layer) would run at high frequency, and another would be lower frequency, running only every n steps.
They had used a recurrent architecture, where these layers would repeat many times ; but to make it work they had to do many approximations, including not fully backpropagating the loss through all layers.
Alexia studied what was useful and what wasn't, and cleaned the architecture as follows :
Why use a recurrent architecture, when you can just make it a loop?
ā”ļø She made the network recursive, looping over itself
Why use 2 latent variables ?
ā”ļø She provides a crystal clear explanation : the one that changes frequently is the reasoning, the one that changes at low frequency is the proposed answer.
ā”ļø She runs ablation studies to validate that 2 is indeed optimal.
This new setup is a much more elegant way to process reasoning than generating huge chains of tokens as all flagship models currently do.
This might be the breakthrough we've been awaiting for so long!
A lone scientist just proposed Tiny Recursive Model (TRM), and it is literally the most impressive model that I've seen this year.
ā”ļø Tiny Recursive Model is 7M parameters
ā”ļø On ARC-AGI, it beats flagship models like Gemini-2.5-pro
Consider how wild this is: Gemini-2.5-pro must be over 10,000x bigger
and had 1,000 as many authors š (Alexia is alone on the paper)
What's this sorcery?
In short: it's a very tiny Transformers, but it loops over itself at two different frequencies, updating two latent variables: one for the proposed answer and one for the reasoning.
@AlexiaJM started from the paper Hierarchical Reasoning Model, published a few months ago, that already showed breakthrough improvement on AGI for its small size (27M)
Hierarchical Reasoning Model had introduced one main feature:
š Deep supervision
In their model, one part (here one layer) would run at high frequency, and another would be lower frequency, running only every n steps.
They had used a recurrent architecture, where these layers would repeat many times ; but to make it work they had to do many approximations, including not fully backpropagating the loss through all layers.
Alexia studied what was useful and what wasn't, and cleaned the architecture as follows :
Why use a recurrent architecture, when you can just make it a loop?
ā”ļø She made the network recursive, looping over itself
Why use 2 latent variables ?
ā”ļø She provides a crystal clear explanation : the one that changes frequently is the reasoning, the one that changes at low frequency is the proposed answer.
ā”ļø She runs ablation studies to validate that 2 is indeed optimal.
This new setup is a much more elegant way to process reasoning than generating huge chains of tokens as all flagship models currently do.
This might be the breakthrough we've been awaiting for so long!
- 4 replies