Reinforcement Learning
Transformers
English
post-training
distillation
agentic-coding
composer-2.5
cursor
kimi-k2
grpo
dapo
diloco
openenv
trl
verl
research
methodology
Instructions to use Codeseys/composer-replication-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Codeseys/composer-replication-framework with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Wave 21: deep-read critical review — 8 source clusters re-read, findings verified
Baladithya Balamurugan +1: Claude Fable 58 Sonnet readers re-fetched every primary source (Composer 2.5 blog verbatim, Composer 2 techreport 2603.24477 full HTML, SWE-smith/SWE-Gym/R2E-Gym/SWE-bench, SDPO/OPSD, GRPO family + Comedy-of-Estimators, DiLoCo line, world-model cluster + anti-evidence, LATS/ToT/rStar/Tree-GRPO/SWE-Search/Symphony/Socratic-SWE, TRL/verl/SkyRL live docs, SWE-MiniSandbox) and cross-checked the repo's research notes + vision docs against them. 2 adversarial critics (fidelity, design); every P0/P1 finding independently verified against code + source quotes — 0 refuted. (Feasibility critic stalled on capacity; flagged unreviewed.) Top verified findings: - SDPO "mathematically the same" claim is WRONG (Cursor cites it as background; ours is a third, blog-inspired design) — to be corrected. - Envisioned tree pipeline had 4 structural breaks: seed-trace/oracle disjointness, NO rollout harness (SFT corpus had no producer), divergence gate uncomputable (whitespace-stub normalizer), no Sandbox.fork(). - …2a16b30