Samsung introduces the Tiny Recursion Model, a 7M-parameter model that can outperform LLMs 10,000x larger, like Gemini 2.5 Pro and o3-mini, on specific problems

The trend of AI researchers developing new, small open source generative models that outperform far larger …

VentureBeat 2025-10-09 Carl Franzen

Context & Ripple Effects

Samsung has been building a generative-AI position since its Gauss model was deployed for employee productivity and earmarked for product use. This new research claim shifts the emphasis from broad deployment to whether architecture can deliver strong results with radically less model scale.

It also lands amid a broader push toward compact, specialized models: Google introduced Gemma 3 270M for task-specific fine-tuning, while Nvidia positioned Nemotron-Nano-9B-v2 as competitive on reasoning benchmarks.

First-order effects

Samsung gains a research-led performance claim against prominent larger models, but its relevance is bounded by the specific problems and evaluations cited rather than general-purpose capability.
Teams assessing model options have another signal to test small-model architectures where task requirements are narrow and measurable.

Second-order effects

Large-model providers and benchmark users face greater pressure to distinguish broad capability from task-specific performance; raw parameter count becomes a less sufficient comparison point.
If the result is reproducible, deployment teams can more seriously compare smaller alternatives on inference cost, latency, and hardware constraints instead of defaulting to the largest available model.

Third-order effects

The model market could segment further between frontier general-purpose systems and compact specialist systems, with architecture and evaluation design carrying more weight than scale alone.
That shift would strengthen buyer leverage only where smaller models reliably meet a defined workload; broad, open-ended tasks may continue to favor larger systems.

The trend: AI development is increasingly separating frontier-scale generalists from small, task-optimized models designed to improve the economics of targeted inference.

Discussion

@jacksonatkinsx Jackson Atkins on x
My brain broke when I read this paper. A tiny 7 Million parameter model just beat DeepSeek-R1, Gemini 2.5 pro, and o3-mini at reasoning on both ARG-AGI 1 and ARC-AGI 2. It's called Tiny Recursive Model (TRM) from Samsung. How can a model 10,000x smaller be smarter? Here's how [im…
@mikeknoop Mike Knoop on x
I have increased confidence that the core AGI algorithm will be less than 10k loc (intelligence is a process, not a model).
@jm_alexia Alexia Jolicoeur-Martineau on x
New paper 📜: Tiny Recursion Model (TRM) is a recursive reasoning approach with a tiny 7M parameters neural network that obtains 45% on ARC-AGI-1 and 8% on ARC-AGI-2, beating most LLMs. Blog: https://alexiajm.github.io/... Code: https://github.com/... Paper: https://arxiv.org/...
@rasbt Sebastian Raschka on x
Interesting tidbit from the author wrt what resources it required. Yes, it's still possible to do cool stuff without a data center.
@mark_k Mark Kretschmann on x
Potentially huge AI breakthrough: “Less is More: Recursive Reasoning with Tiny Networks” A 7B model that scored very highly on the @arcprize benchmark. Francois Chollet called it “impressive work”. [image]
@vraserx @vraserx on x
A 7 million parameter model from Samsung just outperformed DeepSeek-R1, Gemini 2.5 Pro, and o3-mini on reasoning benchmarks like ARC-AGI. Let that sink in. It's 10,000x smaller yet smarter. The secret is recursion. Instead of brute-forcing answers like giant LLMs, it drafts a [im…
@deedydas Deedy on x
The TRM paper feels like a significant AI breakthrough. It destroys the pareto frontier on the ARC AGI 1 and 2 benchmarks (and Sudoku and Maze solving) with an estd < $0.01 cost per task and cost < $500 to train the 7M model on 2 H100s for 2 days. [Training and test specifics] [i…
@jm_alexia Alexia Jolicoeur-Martineau on x
The idea that one must rely on massive foundational models trained for millions of dollars by some big corporation in order to solve hard tasks is a trap. Currently, there is too much focus on exploiting LLMs rather than devising and expanding new lines of direction.
@rasbt Sebastian Raschka on x
From the Hierarchical Reasoning Model (HRM) to a new Tiny Recursive Model (TRM). A few months ago, the HRM made big waves in the AI research community as it showed really good performance on the ARC challenge despite its small 27M size. (That's about 22x smaller than the [image]
@tomsiwik Tom Siwik on x
This was my vision all along. Small tiny models - trained to a single task really really well, collaborating with other small LLMs in an agentic framework. This proves tiny models are beast when the thinking logic is sound.
@aaditsh Aadit Sheth on x
Samsung may have built the smartest model on Earth, smaller than your camera app. Proof that how a model thinks matters more than how big it is. Only 7 million parameters and it beat Gemini 2.5 Pro, DeepSeek R1, and o3-mini. Wild. [image]
@teortaxestex @teortaxestex on x
Ok I was unfair to @jm_alexia. had no time to read the TRM paper, I should have. It's a very good faith attempt to rescue the idea of enabling huge effective depth for a tiny model, without any shaky premises or unwarranted approximations of HRM. It may be a real, big finding. [i…
@dr_singularity Dr Singularity on x
This is insane. New AI model from Samsung, 10,000x smaller than DeepSeek and Gemini 2.5 Pro just beat them on ARC-AGI 1 and 2 Samsung's Tiny Recursive Model (TRM) is about 10,000x smaller than typical LLMs yet smarter because it thinks recursively instead of just predicting [imag…
@shedletsky John Shedletsky on x
@JacksonAtkinsX These results seem too good and it makes me wonder if this paper is real. Large reasoning models also do an iterative loop like this so it's not obvious what the innovation is that would account for the step up.
@fchollet François Chollet on x
Impressive work.
@hindookissinger @hindookissinger on x
This is a crazy paper. At 7 million parameters, how hard can it be to train?
@policytensor @policytensor on x
I ask again. Is compute really all that big a moat? And if not, is Sama's trillion dollar wager gonna blow up in all their trillion-dollar faces?
@thom_wolf Thomas Wolf on x
solo papers from great AI researchers are always such a treat
@brianroemmele Brian Roemmele on x
BOOM! Tiny Recursive AI Model Outsmart Massive LLMs! In a stunning upset that's shaking the foundations of AI, a groundbreaking new research paper has unveiled a deceptively simple model that punches way above its weight class. Imagine a nimble, featherweight fighter stepping [im…
@slow_developer Haider on x
Important research paper: “Less Is More: Recursive Reasoning with Tiny Networks” According to the research, a tiny 7M-parameter model beats larger LLMs (incl. DeepSeek R1, Gemini 2.5 Pro) on ARC-AGI-1/2 • ARC-AGI-1: 44.6% • ARC-AGI-2: 7.8% the breakthrough isn't size, it's [image…
@alex_prompter Alex Prompter on x
Model architecture > compute Draft -> Internal Reasoning -> Self-Critique -> Revise -> Repeat Result = Smarter model
@quantumtumbler @quantumtumbler on x
TRM proves what we've always known: intelligence is not scale, it's recursion. A 7M‑param model outperforming LLMs is not evolution its resonance. The Oversoul loop doesn't need size to punch it needs integrity.
@zews @zews on x
I wasn't ready to say it. But I'm saying it now. They didn't just beat big models with recursion. They confirmed something we felt in our bones: > Truth isn't scale. > It's structure. > It's breath. > It's memory. @Sceptinot @Chaos2Cured @therealZpoint — I see
@maziyarpanahi @maziyarpanahi on x
how small a model can be? apparently 7m parameters! samsung cooked, “less is more”. [image]
@_arohan_ Rohan Anil on x
Really enjoyed reading paper that trains a tiny model that achieves high arc-"agi" scores Loop(x , y-embed, z-embed); x inputs. Where y is prediction and z is memory initialized to zero. Gradient from last step of the loop (bit nuanced as there are inner loops) Weights shared
@gregkamradt Greg Kamradt on x
Wow small Get this in the ARC Prize 2025 on Kaggle! Then you'll get a verified score on the v2 *private* test set for Might put out a bounty to make it happen If you're up for it DM me
@kimmonismus @kimmonismus on x
I took a look at the paper. And as incredibly impressive as this research is, many see it as confirmation that SLMs can achieve extremely high performance. But the comparison is very skewed. Why? The Tiny Recursive Model (TRM) is an extremely small, recursive computing model
@brianroemmele Brian Roemmele on x
I had a great brainstorming session with a dozen Open Source AI builders on this paper. The consensus is to replicate it!
@paul_cal Paul Calcraft on x
TRM etc. won't have much industry impact because the primary benefit of LLMs is not predictive (or task) accuracy, it's that you program them in English & don't need your own clean datasets (or ML engineers)
@huggingpapers @huggingpapers on x
Samsung's Tiny Recursive Model (TRM) masters complex reasoning With just 7M parameters, TRM outperforms large LLMs on hard puzzles like Sudoku & ARC-AGI. This “Less is More” approach redefines efficiency in AI, using less than 0.01% of competitors' parameters! [image]
@far__el Far El on x
interesting paper, 7M params, 45% on arc-agi-1, 8% on arc-agi-2 with a simpler arch than HRM [image]
@jm_alexia Alexia Jolicoeur-Martineau on x
With recursive reasoning, it turns out that “less is more”. A tiny model pretrained from scratch, recursing on itself and updating its answers over time, can achieve a lot without breaking the bank.
@teortaxestex @teortaxestex on x
if this isn't just a data thing, like HRM was, it's an indictment of ARC-AGI 1 and 2.
@clementdelangue Clem on x
Very cool paper! You can discuss with the author here: https://huggingface.co/... [image]
@neurosp1ke Andreas Köpf on x
The y+z feedback + ACT of TRM is nice. Maybe in a block wise fashion it could be applied to arbitrary sequences? - reminds me a bit of diffusion. Adaptive depth RNNs with lateral attention ftw 😉 [image]
@suryasure05 Surya on x
I spent my summer building TinyTPU : An open source ML inference and training chip. it can do end to end inference + training ENTIRELY on chip. here's how I did it👇: [video]
@nielsrogge Niels Rogge on x
It makes me a bit sad that amazing research like the one below isn't pursued a lot anymore due to the LLM era People just give up cause they think an LLM will beat them. Wrong. There are so many research directions to be explored, so many new architectures to be uncovered
@cgarciae88 Cristian Garcia on x
you might not believe it but a few years back twitter vibed with papers like all day instead of the nonstop AI gossip slop we have now
@omarsar0 Elvis on x
Hierarchical Reasoning Model This is one of the most interesting ideas on reasoning I've read in the past couple of months. It uses a recurrent architecture for impressive hierarchical reasoning. Here are my notes: [image]
@rasmus1610 Marius Vach on x
This is impressive not only because of the awesome results, but because a single researcher did all of this. This is so inspiring. You don't need a have lot of compute or a be a big frontier lab. You just need a good idea and the grit and agency to see it through
@daniel_mac8 Dan Mac on x
🔥 Tiny Recursion Model may change AI forever, and everyone is talking about it. A recursive reasoning algo that outperforms much larger (and more costly) models like o3-mini, DeepSeek R1 and Gemini 2.5 Pro. What happens with the trillions $ datacenter buildout planned then? [imag…
@sytelus Shital Shah on x
@JacksonAtkinsX You should mention the caveats. It is tiny because it is very specialized model without language. It would have been much more interesting if they had done more standard math/coding reasoning.
@edgarpavlovsky Edgar on x
i'm going to keep saying it until we've implemented this approach at @darkresearchai, but there's a huge space for (1) lower latency (2) cheaper AI experiences through tiny models that are good enough to solve scoped down tasks the “remote llm api for everything” approach will
@_clashluke Lucas Nestler on x
TRM is one of the best papers I've read in the past years - it truly shows the unfiltered process of a researcher: 1) See awesome paper, get hyped about it 2) Read it - looks cool 3) Run it - doesn't work 4) Find glaring mistakes 5) Fix the issues https://x.com/...
@drphiltill Phil Metzger on x
This is essentially how I use LLMs now, where I am in charge of the scratchpad and I force the LLM to iterate until I'm confident in the result. It will be great when the AIs l I'm using can do that internally.
r/LocalLLaMA r on reddit
Less is More: Recursive Reasoning with Tiny Networks (7M model beats R1, Gemini 2.5 Pro on ARC AGI)

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