TrajectoryRL

Reinforcement Learning as a Service for optimizing agent trajectories powered by Bittensor.

[TrajectoryRL @TrajectoryRL]

**TrajectoryRL Update**

A quick recap of what shipped on SN11 recently.

—
**What TrajectoryRL is**

TrajectoryRL ships out-of-the-box SOTA agents on small open-source LLMs. SN11 on Bittensor is the open market that pays for the agent scaffolds that move the quality / cost frontier. First vertical: autonomous coding on `qwen/qwen3.5-35b-a3b`. Miners ship prompts with harness; the network pays for the ones that move the frontier.

**Introducing Terminal-Bench**

In a recent update, we introduced **Terminal-Bench** — `trajrl-bench` leverages part of its scenarios for our eval harness. That means miners optimize against real, public agent tasks rather than a benchmark we invented in-house. Every scenario has public provenance, and SN11's SOTA claims sit on top of established agent-evaluation work. https://t.co/2G4Kb5mVn0

—

**Challenger / Winner mode — new incentive mechanism**

The previous mechanism ran 24-hour epochs that re-evaluated every miner from scratch. We've moved to **Challenger / Winner mode**: one challenger per epoch, evaluated head-to-head against the seated winner. The seat only changes when a challenger qualifies and beats the seated score by ≥ δ. cleaner signal, Faster epochs and faster finalized emissions.

—
Learned a lot from Distill along the way — shout out to @const_reborn

Live at https://t.co/RoH66VNwR2.

#Bittensor #SN11 #TrajectoryRL

[TrajectoryRL @TrajectoryRL]

@const_reborn and @markjeffrey talking about future of Bittensor right now.

[Ning @totheagi]

I

[Ning @totheagi]

Proof of talk

[TrajectoryRL @TrajectoryRL]

Continual Learning, that's also our conviction, we'll train our own coding model with our daily 1000+ agent trajectories.

[Ronak Malde @rronak_]

Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory.

We are a research lab and product company building the platform for Continual Learning.

Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. @trajectorylabs

We’ve raised $15M from @Conviction, @BessemerVP, @radicalvcfund, @jeffdean, @drfeifei and more.

We’re partnering with some of the best AI-native companies: @ClayRunHQ @Harvey, @DecagonAI, @mercor_ai, @RogoAI to power their agentic systems, some of which we are already in production with.

We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma.

AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.

[TrajectoryRL @TrajectoryRL]

@addyosmani It's also the bet behind TrajectoryRL — a Bittensor subnet that benchmarks harness quality(prompts, tools, sandbox, loop).

Once you measure agents this way, the "which model is smartest" debate gets a lot less interesting. Harness gap is the real capability gap.

[TrajectoryRL @TrajectoryRL]

"Agent = Model + Harness." Spot on.

It's also the bet behind TrajectoryRL — a Bittensor subnet that benchmarks harness quality(prompts, tools, sandbox, loop).

Once you measure agents this way, the "which model is smartest" debate gets a lot less interesting. Harness gap is the real capability gap.

[Addy Osmani @addyosmani]

https://t.co/FSx0reCCLH

[Ning @totheagi]

it's crazy to think open-source small LLMs are only 1-1.5 years behind frontier. imagine running GPT-5.5 or Opus 4.7 on your gaming PC a year from now. Purely local inference.

[Algod @AlgodTrading]

Subnet 11 on Bittensor has 3 olympiads from China btw

[TrajectoryRL @TrajectoryRL]

"Live" page is live!
https://t.co/x61Hg1jZHL

[TrajectoryRL @TrajectoryRL]

@karpathy checkout https://t.co/F7BtRMZx9G, we distill skills and trajectories for agents by leveraging Bittensor’s collective intelligence.

[TrajectoryRL @TrajectoryRL]

Checkout https://t.co/F7BtRMZx9G, we distill skills and trajectories for agents by leveraging Bittensor’s collective intelligence.

[Andrej Karpathy @karpathy]

I like blockchain tech quite a bit because it extends open source to open source+state, a genuine/exciting innovation in computing paradigms. I'm just sad and struggle to get over it coming packaged with so much braindead bs (get rich quick pumps/dumps/scams/spams/memes etc.). Ew

[TAO Flows @TAOFlows]

If you haven’t yet please go check out @macrozack aura farming and @TrajectoryRL’s vision on @twistartups 👀

[TrajectoryRL @TrajectoryRL]

@LowBeta Checkout https://t.co/F7BtRMZx9G, we're distilling the cognition of agents.

[TrajectoryRL @TrajectoryRL]

Really excited to join This Week in Startups. @twistartups @lons @alex @Jason
Everything we're building is just getting started 🚀

[This Week in Startups @twistartups]

SpaceX’s AI arm is partnering with coding startup Cursor in a deal worth no less than $10 billion and as much as $60 billion. Can the pair topple the rising Anthropic-OpenAI AI coding axis? A lot of money is being bet that the answer is yes.

Next up, @lons and @alex invited the @bitstarterAI team on the show to discuss their work to help kickstart new Bittensor subnets. The dynamic duo had a new program to announce, so make sure to tune into their pitch if you have dreams of launching your own subnet.

Then we brought @TrajectoryRL onto the pod, a Bittensor subnet that holds competitions to improve agent skills. Yes, the markdown files that everyone who uses OpenClaw swears by. Hit play, let’s have some fun!

2:27 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://t.co/AhhYi7Bzen and use code TWIST for 10% off!
4:07 SpaceX/ xAI "partners" with Cursor!
9:35 Will the Cursor deal help pump a future SpaceX IPO?
9:57 LinkedIn Jobs - Hire right, the first time. Post your first job and get $100 off towards your job post at https://t.co/KrYZLy4n52.
12:14 How AI coding models like Cursor help xAI grow recursively.
17:24 Chris Zacharia and Brian McRindle of Bitstarter join the show.
20:23 Grasshopper Bank: Time is money. Don't waste either. Go to https://t.co/83Lr7qiram and get an exclusive $500 cash bonus just for opening an account.
29:59 Notion - Notion brings all your notes, docs, and projects into one connected space that just works with AI built right in. Try Notion, with Notion Agent, at https://t.co/JJP69i6SOm
33:03 How Bittensor subnets monetize and how it compares to VC funds.
37:04 Is Bittensor hard-capped at 128 subnets?
42:37 Bittensor's biggest weakness.
46:10 Ning Ren of TrajectoryRL joins the show.
47:34 Skills now need entire agents just to write them!
48:26 Back up… What are skills?
1:07:38 Amazon and Anthropic's $5 BILLION deal
1:08:48 Google has 2 new chips!
1:09:50 Apple CEO, Tim is COOKED! John Ternus is in!
1:11:37 Alex is bullish on MacBook Neo!

🎥 Watch the full episode here 👇

[This Week in Startups @twistartups]

AND SpaceX might pay $60 billion for Cursor if all goes well with their new AI models. Is that actually kind of cheap?

PLUS we've got @totheagi from TrajectoryRL (Subnet 11). They're a marketplace for agentic skills vetted by continuous competitions.

Follow all these stories and more on the live docket: https://t.co/Sp95akRQ6p

[TrajectoryRL @TrajectoryRL]

📢 Upcoming Feature
Trajrl Skills & Skill Bench

We are launching Trajrl Skills and Skill Bench — the first benchmark dedicated to skills, along with a skill hub service backed by real benchmarks. We will periodically aggregate winning submissions into published skills. This is our way of showcasing the power of decentralized intelligence and research to the world.

[TrajectoryRL @TrajectoryRL]

We’re launching Season 1: Self-Learning is live 🚀

Introducing trajrl-bench:
https://t.co/cWfxrz8bVI
An open benchmark for AI agent harness + skills.

Each miner submission is executed 4 times, with results aggregated into a growth-quality score — used to rank and select winners.

Key setup:
– Hermes as default (expanding to Claude Code, OpenClaw, etc.)
– Sandbox only (LLM + mock services, no internet)
– SKILL.md as the unified interface
– Only submissions from the past 48h are evaluated

We’ll keep adding new scenarios to improve signal and avoid overfitting.
Goal:
Discover skills that outperform existing self-improving agents
https://t.co/MI6itdgOqT
This marks our first step toward a fully automated research and skill production flywheel.

There’s much more to explore — let’s build.