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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fix(phase-8): close all 5 cross-family final-verify findings + regression tests
The deep-work-loop exit gate (2 independent confirmations). A final 3-family scatter of the session diff found and we fixed: - [P0 latent] SDPO sentinel-mask used only student_response_valid; now requires BOTH valid masks AND non-sentinel indices (a future divergent teacher tail could have distilled against clamped position 0). - [P1 convergent, GPT-5.5+Gemini] curriculum shared one n_effort denominator for turns+think_tokens; split into independent n_turns/n_think counters. - [P1] docker E2E test ran pip-install under --network none (would fail when the gate activates); replaced with stdlib python -c expression runner, no network. - [P1] MMLU reward: Answer: A or B hedge scored full credit; _HEDGE_RE now penalizes it as multiple-answers. - [P1] HackMonitor read-markers matched import in important / cat in concatenate and scanned the agent patch as read-evidence; whole-word regex + exclude patch/diff payloads from BOTH layers (a legit patch can no longer self-incriminate…678d10b -
feat(wave-b): ADR-013 LMA integration + B4 end-to-end SDPO-fires proof + doc refresh
ADR-013: composer_replication/integrations/altered_minds/ (generic, framework-side) - MMLUFormatReward: structured-answer reward, scores only the final letter (not rationale style), unparseable/multiple-answer penalties, option-order randomization, always-C exploit detectable via logged option distribution. - dual_kl_logger: KL(policy||altered-init) AND KL(policy||unaltered-base) as the washout-vs-amplification instrument (neither optimized). - channel_ladder_configs: A0-A4 isolated-channel ladder (alpha=0.02/beta=0.05), replacing the uninterpretable combined alpha=0.2/beta=0.4 recipe per the cross-family research critique (SDPO can AMPLIFY an altered model). B4: examples/altered_minds_channel_ladder/run.py PROVES the SDPO channel FIRES nonzero (JSD=0.0565, grad flows) through the REAL shipped collator alignment indices — the thing the old smoke could not show (it only proved init). Honest stub-with-differing-tokens proof; real-model path gated behind ALTERED_MINDS_REAL_MODE…21647a4