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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Deep Work Loop Log — Composer 2.5 Replication Framework
Started: 2026-05-26
Operator: Codeseys (Hermes Agent autonomous loop)
Skill: deep-work-loop v1.0.0
Vision
Take any HuggingFace model → further RL train it using:
- RLVR (tests-pass reward),
- SDPO/hint-distillation (Composer 2.5's "targeted RL with textual feedback"),
- multi-teacher trace-replay DPO, integrated against TRL/VeRL/OpenEnv with DiLoCo-style outer loop sync.
Output: a published, reproducible framework — the "Composer 2.5 replication" the open ecosystem is missing.
Starting state
- HEAD:
040eff8(Wave 6: vision validation self-audit, 5/10 scorecard) - Tests: 38/38 green in
spikes/005-integrated-trainer-skeleton/ - Working tree: clean
Phase ledger
| Phase | Description | Status | Started | Done |
|---|---|---|---|---|
| 1 | commit-state | ✅ | 2026-05-26 | 2026-05-26 |
| 2 | backlog-audit (BACKLOG.md from VISION_VALIDATION) | ✅ | 2026-05-26 | 2026-05-26 |
| 3 | parallel-research (3 subagents) | 🟡 | 2026-05-26 | |
| 4 | architect with ADRs (ADR-001..003) | ⏳ | ||
| 5 | plan in waves (W7–W10) | ⏳ | ||
| 6 | execute W7 — Spike 006 (real HF model smoke) | ⏳ | ||
| 7 | execute W8 — Spike 007 (real trace ingestion) | ⏳ | ||
| 8 | execute W9 — Spike 008 (DiLoCo smoke) | ⏳ | ||
| 9 | execute W10 — packaging | ⏳ | ||
| 10 | (Modal-gated) Spike 002a-mini real GPU smoke | ⏳ | ||
| 11 | cross-model-final-review | ⏳ | ||
| 12 | update scorecard + push | ⏳ |
Constraints
- Verify ALL claims against primary sources (Wave 2 lesson — subagent synthesis is not evidence).
- Tests must pass before commit.
- Memory L1 is at 99% — write to L2 wiki + L3 fact_store, not L1.
- Modal budget: $20 hard cap for this loop. Anything more goes to user for approval.
- No
upload_filemixing withgit push—git push hf master:mainonly. - Commit messages via
-F /tmp/<wave>-commit-msg.txt.