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Introducing Adapt-1 Machina: Learning Continuous Control Sequences
With Adapt-1 Machina, we explore how continuous sequences of machine controls can be learned through reinforcement learning, starting with coarse outcome feedback and without demonstrations or a critic.
Can Adapt‑1 Preview stay in an interactive decision loop while thousands of outcomes keep changing its persistent decision state?
We finished migrating to the infrastructure intended for public access. Here’s what latency looks like as retained experience accumulates.
Introducing Emergence.
It removes learner ontology from Adapt-1’s online learning setup. Observation paths, temporal context, causal variables and action-state bindings can now be induced online from interaction and committed as revisioned state.
Blog : https://t.co/t8eOFCzfki
Summary of $REI Token Utility Plans:
• Ecosystem token for access, ownership, attribution, and exchange across the Rei / Unit 00 layer
• Planned settlement currency for marketplaces of Core instances, published knowledge, learned structures, and inspectable traces
• Planned revenue-funded buybacks (not live yet)
• Intended for transparent on-chain marketplace settlements and treasury activity
• Supports future company–token alignment (ownership structure, revenue flows, buyback mechanics)
While the industry keeps scaling frozen models, REI is quietly building something different:
Systems that actually learn after deployment.
Adapt-1 starts nearly blank and improves from real feedback while working.
Latest results show it matching systems trained on 1 million Alchemy episodes — using only 5,000 live decisions and zero task pretraining.
New plasticity rules just went live in the API.
More benchmarks, more evidence, still shipping.
Most models stop learning the day training ends.
This one doesn’t.
Still early. Still a research preview.
But the direction is becoming hard to ignore.
base:0x6b2504a03ca4d43d0d73776f6ad46dab2f2a4cfd
A reward can tell an RL/online learner that something worked without telling it which combination of internal signals made it work.
Today, we’re unlocking two learning rules in Adapt-1 Preview: Counterfactual Utility Plasticity (CUP) and Temporal Context Projection (TCP).
The strength of Adapt-1 doesn't rely on a fixed state, but on the interactions with and within the learning mechanics. Our goal was to make a system that could fully adapt to any form of data, be them static or a stream.
Read from a book or live and learn, it doesn't matter.
PALM contributor, Aakanksha Chowdhery following @rei_labs
Nothing out of the ordinary just academic tech elites impressed from a humble neo-lab valued at ~25 mil mcap
So much higher
$REI
To be honest, i dont think there is a web3 project out there other then $REI with a more competent dev that takes the time to take on all questions regarding the product (on X and TG)
Very comfy hold for me
Grei
(and btw, its the only utility project with a strong community)
Base trenches dead, long live @rei_labs.
They launched a new product, shared robotic benchmarks showing higher score than existing (w no pretraining), made product fully accessible for anyone, and have a guide to replicate the benchmark results for anyone who wants to try
gREI
The $REI team has been cooking hard, and it looks like they’ll slowly start sharing what they’ve been building in secrecy over the last 6–12 months.
Very exciting first share in a while and I have reason to think they’ll surprise a lot of folks.