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
-
Wave 5: full publication-materials drafts (pre-experimental release set)
baladithyabUser asked: draft all publication materials until we can do proper experimentation to back up our ideas. Drafted a complete pre-experimental publication wave: methodology paper, blog post, repo Discussion announcement, X/LinkedIn threads, CITATION.cff + .bib at repo root, plus a RELEASE_CHECKLIST coordinating sequencing, embargo, and risk register. None of this is posted publicly yet — that's a separate decision when ready to ship. Honest framing reused throughout: this is methodology + economic-feasibility + integration architecture, not training results. Spike 002-004 produce the empirical training validation in a v0.1 follow-up. Added: publications/PAPER_v0.md (33KB, ~6500 words) Longform methodology paper: 10 sections covering Composer 2.5 audit, integration architecture, novel TR-DPO channel, spike 001 + 005 results, honest "what's NOT proven" section, reward-hacking proposals, full citation list with primary sources. publications/BLOG_POST.md (16KB, ~2400 words) Hu…639a760