Development ·
Axion makes its agent-training recipe inspectable
A public technical report and linked repository expose task construction, prompts and a two-stage supervised-training plus reinforcement-learning pipeline.
Why it matters
Researchers can inspect how tool-use examples and verified answers are produced, making the training approach easier to examine and attempt to reproduce.
What to watch
Independent reproductions with matched evaluation conditions, complete resource costs and clear provenance for released weights.
Still uncertain. Reported benchmark scores depend on run selection and evaluation conditions. Linked community weights are a separate reproduction; no official checkpoint or independent replication was verified.
Impact assessment reviewed 2026-11-08.
- Who it helps
- Researchers inspecting open agent-training methods.
- Availability
- Paper and repository are public. The linked 35B weights are a community reproduction; the official repository says they are not an official checkpoint.
How the recipe works
Two supervised stages teach reasoning and tool-dependent work, followed by reinforcement learning. Programmatically checked reference answers help filter generated tasks. The repository exposes prompts, data construction and evaluation code.
Axion 1.0 technical report ↗Axion training and evaluation repository ↗
Keep the releases distinct
The community 35B model has its own reproduction results and inference instructions. It has 35 billion total parameters, with a smaller active subset; that does not mean all weights fit in the active subset’s memory. The displayed multi-GPU example was not tested.
Axion 35B community reproduction ↗Axion training and evaluation repository ↗
Read comparisons in context
The project reports selected best-of-several-run results, and some comparison scores come from other reports. Those conditions limit conclusions about a single run or a new task.
What you can do
- Read the source and its evaluation conditions before applying this approach. Open reference ↗
Where the evidence stops
Sources checked November 8. Neither training nor inference was reproduced; hardware requirements, full training costs and model quality remain unverified.
Sources read. Primary text checked November 8, 2026 UTC. No third-party software, model inference or complete workflow executed.
Sources & further reading
Paper · paper
Axion 1.0 technical report ↗
November 2 submission and November 3 revision checked; training and evaluation were not reproduced.
Yuqi Jia and colleagues · page checked 2026-11-08 · Published 2026-11-02
GitHub · implementation
Axion training and evaluation repository ↗
Public recipe includes data construction, filtering, prompts, training and evaluation. README explicitly distinguishes community weights from official checkpoints.
ByteDance / Axion team · page checked 2026-11-08
61 stars · observed 2026-11-08 11:34 UTC
Reddit · discussion
Question about Apex and Axion 35B ↗
Original question inspected. Exact publication timestamp and score were unavailable; asking for experiences does not verify performance.
u/blue2020xx · page checked 2026-11-08
Web · analysis
Axion project ↗
Benchmark presentation includes best-of-three or best-of-five runs and mixed baseline sources. Comparisons need those conditions.
Axion authors · page checked 2026-11-08
More sources reviewed (1)
Hugging Face · implementation
Axion 35B community reproduction ↗
Community model card inspected. Its weights are a separate reproduction, not an official checkpoint. Eight-GPU inference example is not a consumer-memory guarantee.
Axion1-0 · page checked 2026-11-08
Coverage note: Independent dated demonstrations, cross-platform attention and complete execution remain unverified.
The record over time (1)
Dated source checks and published explanations. An observation date can differ from the original release date.
- · Added to Current
2026-11-02: A public technical report and linked repository expose task construction, prompts and a two-stage supervised-training plus reinforcement-learning pipeline.
Assessment: Researchers can inspect how tool-use examples and verified answers are produced, making the training approach easier to examine and attempt to reproduce.