AfterQuery

This account is verified because it's an official organization on X. Learn more

@AfterQuery

Applied research lab curating data solutions to accelerate foundation model development.

afterquery.com Joined February 2025

11 Following 4K Followers 101 Posts

Latest posts 5

Pinned post

AfterQuery @AfterQuery Jun 12

Congratulations to @nvidia on the release of Nemotron 3 Ultra!

Ultra now leads GDPval-AA by a good margin among US open source models. @AfterQuery is proud to have provided the GDPval training data used to hill climb the benchmark.

AfterQuery @AfterQuery 5d

Excited to have one of our post-training runs showcased by @thinkymachines.

Using Tinker, we post-trained gpt-oss-20b with SFT, then RL on our terminal agent data. Its Terminal-Bench 2.0 pass rate went from 3.1% to 17.0%.

@AfterQuery datasets are validated with Tinker against held-out benchmarks before they ship.

If you're building frontier models and want data with measured impact, reach out at research@afterquery.com.

AfterQuery @AfterQuery Oct 29

We improved models on professional work tasks using NVIDIA AI’s xToken, NeMo Gym, and NeMo RL for cross tokenizer distillation.

Read the full blog below. x.com/i/article/2115…

AfterQuery @AfterQuery Oct 29

We used @NVIDIAAI's xToken recipe along with NeMo Gym and NeMo RL open post-training libraries to improve models on professional work tasks through cross tokenizer distillation.

Held-out GDP.pdf strict pass@1 (avg 5) in NeMo Gym:
- Nemotron 3.5 Super VL: 6.67% to 11.83%
- Qwen3.5-9B: 4.40% to 8.20%

Read the full blog below.

0:12

AfterQuery @AfterQuery Oct 27

Congratulations to @Recursive_SI on the release of Flint!

Flint runs locally on your laptop and competes with models that require 10x the compute.

@AfterQuery is proud to support the @Recursive_SI team.

Yuandong Tian Verified account @tydsh Oct 21

Today, we release our first model, Flint-40B-A2B, a fast local coding model built with our automated research system. huggingface.co/RecursiveSI/fl…

1. Strong performance. 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, and 74.2% on SWE-bench Verified.

2. It is trained from scratch (tokenizer->Pretrain->Midtrain->SFT->RL).

3. Fast in your local laptop. Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark.

4. We build the complete model training pipeline in 4 month!
View quoted post on X