8 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().
- …
Baladithya Balamurugan+1: Claude Opus 4.8 (1M context)
41289bf
Closes the two cheapest "next-level" gaps from the F5 fidelity audit
(Rubric A items c2, h, i). Both reproduce Composer-2 §4.1/§4.2 exactly,
without the hint mystery. 33 new tests; full suite 415 passed / 66 skipped.
(1) k1-in-reward KL (F5 #1, the highest-fidelity-leverage single change).
Composer-2 §4.1 chooses the k1 estimator IN THE REWARD (-log r); TRL uses
k3 IN THE LOSS. The 2025/26 evidence (arXiv:2512.21852, verl k1-only, TRL
#4967) says k1-in-reward improves OOD generalization where k3-in-reward can
collapse — exactly the "take any model to the next level" axis.
- trainer/kl_in_reward.py: pure, tested math. k1/k3 per-sequence
estimators + apply_kl_in_reward, which adjusts GRPO advantages by
-coef·(KL - group_mean(KL)). test_kl_in_reward.py PROVES this exactly
equals folding -coef·KL into the reward then re-running GRPO's
group-mean baseline (9 group/gen combos) — the justification for not
forking TRL's scoring code. Exact only without std-norm (Dr.GRPO …
Baladithya Balamurugan+1: Claude Opus 4.8 (1M context)
50e3f0e
Answers the dataset-gen+SFT-vs-RL-vs-BOTH question with 5 grounded
facet designs from the aws-native-architecture-design workflow:
- F1 systems-framing: BOTH = two loops at two timescales, not two phases;
component->loop mapping table; SFT-first ordering; S3 dataset contract
(6 typed prefixes); repo delta (tree_controller, s3_layout, sft_floor).
- F2 aws-datagen: per-stage native verdict (Glue 5.0 Spark ingest ->
Bedrock batch replay -> AWS Batch array on Spot test-exec -> EMR
Serverless normalize), Step Functions orchestration.
- F3 rl-sagemaker: runnable-now g5.2xlarge GSM8K GRPO smoke (<$1),
colocated vLLM, BYO container from PyTorch DLC; live-verified quota.
- F4 decoupled-diloco-s3: N single-instance jobs, ObjectStoreAllReduce
PUT-poll-mean over S3, strong-consistency correctness, IAM gotcha,
cheapest validating-run ladder.
- F5 fidelity-audit: rubric vs Composer 2.5/Composer-2 + load-bearing
papers; 5 FULLY-REPLICATED, k3-vs-k1 KL documented infidelity,
missing…
R14: harden live-Docker tests against transient daemon contention
Baladithya Balamurugan+1: Claude Opus 4.8 (1M context)
c647fe9
The Phase-8 verifiers flagged 2 live-Docker DockerSandbox tests as flaky under
concurrent container resource pressure (full suite + review agents spawning
containers) — passing in isolation. Added _retry_docker(): bounded retry (3x,
backoff) on transient daemon errors (timeout/resource/conflict/500/connection),
re-raising genuine repeatable failures. Wired into the 4-gate inversion test and
the network-disabled test. 27 passed / 1 skipped, lint-clean.
Closes the last addressable Phase-8 verification finding; backlog now fully
resolved on this host (remainder is user-gated GPU/token only).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Baladithya Balamurugan+1: Claude Opus 4.8 (1M context)
7a55e1e
Built by the parallel execution team (worktree-isolated), integrated + tested here.
New modules (all CPU-testable via mock/lazy-import; optional deps gated):
- composer_replication/safety/ — HeldOutGuard run-level collapse kill-switch
(held-out-declines-while-reward-rises streak + KL-to-init hard-stop 0.08 +
proxy-real hacking-gap; EMA-denoised, latched-fire, CollapseStopError). The
documented #2 safeguard for the self-evolving flywheel. 23 tests.
- composer_replication/diloco/serverless/eks.py — EKSExecutor satisfying the
ServerlessExecutor Protocol via a SINGLE Kubernetes Indexed Job → N rank-ordered
ReplicaHandles, gang-cancel (Background propagation), REPLICA_RANK via the
downward API, S3 rendezvous (IRSA). 28 tests (mock BatchV1/CoreV1).
- composer_replication/diloco/serverless/sagemaker.py — SageMakerExecutor
(boto3 create_training_job, same S3 rendezvous, status mapping). +13-test
module written during integration (the build agent shipped it test-less).
- compos…
Baladithya Balamurugan+1: Claude Opus 4.8 (1M context)
c11cf49
Backlog-resolution Wave 1 (branch backlog/goal-resolution-2026-06).
Bugs fixed:
- B1 (P0): generate spikes/007 synthetic_session_with_error.jsonl fixture
(never committed — .gitignore *.jsonl whitelisted the sibling but not this
one) → the 8 failing test_trace_examples_adapter tests now pass. Added the
missing .gitignore whitelist line.
- B2 (P2): [dev] extra was un-installable on Apple Silicon (pulled Linux-only
torchft-nightly). Dropped diloco from base [dev]; added [dev-full] for Linux.
- B3 (P2): [serverless] extra now includes s3fs/boto3/kubernetes (needed for
real S3 rendezvous + the EKSExecutor/SageMakerExecutor adapters).
Newly unblocked:
- D1 (…-245d): Docker substrate E2E — Docker now available on host; both gates
(4-gate substrate inversion + cache-scrub-in-container) PASS. Long-standing
"hardware-blocked" item closed.
Doc/API debt:
- B4: reconciled divergent test counts (115/176/210/232) to one canonical
figure measured this session: 266 passed / 62 skipp…
architect: ADR-011/012/013 + research (alignment-index fix, review-findings closure, LMA channel-ladder)
Phase-3 of the deep-work-loop bringing Composer 2.5's dataset-generation and
targeted-RL-with-textual-feedback methods into the framework. 5 new research
docs (130KB), all with primary-source citations:
- 06-feature-deletion-datagen.md: Feature Deletion env design (online pass-rate
difficulty gate, reward-hacking safeguards, 5 OSS substrates w/ HF ids +
licenses, deletion mechanics, FeatureDeletionEnv + TRL reward_fn adapter).
- 07-sdpo-hint-generator.md: layered HintGenerator (template -> raw-error ->
LLM-judge -> introspection -> learned -> SDPO sibling-bootstrap), actual
template strings, judge prompt, slots into existing CollatorConfig hook.
- 08-sdpo-grpo-integration.md: ComposerGRPOTrainer(GRPOTrainer) design adding
SDPO KL at error turns on top of Dr. GRPO; PRIME-RL recipe blocked (log-probs
only), TRL subclass is the host; CPU smoke plan.
- 09-composer-blog-delta-2026.md: blog re-extraction delta; found the Composer 2
arXiv tech report; SDPO successful-rollout-as…
Integrate Cursor blog directly + audit research note + add SDPO/OPSD link
baladithyab
1cede23
User flagged a real gap: I had dispatched a subagent to research Composer 2.5
but never personally read the Cursor blog. Audit findings:
1. The targeted-textual-feedback method = published SDPO (Hubotter et al.,
arXiv:2601.20802, ICLR 2026 Workshop) + OPSD (Zhao et al., arXiv:2601.18734,
code at github.com/siyan-zhao/OPSD, MIT). Cursor cites these directly in
footnote 1 of the blog. The subagent's research note never mentioned them.
This is huge: there's published, MIT-licensed code for Composer's secret sauce.
2. Several claims in research/01-composer-2.5.md are NOT in the Cursor blog
and were extrapolated from secondary sources ("85% post-training compute",
"Anyrun" environment name, specific benchmark scores, "PPO or GRPO variant").
Likely correct via community consensus but not blog-verified.
3. Composer hint-distill (single model, hint-conditioned context) and
trace-replay-distill (N external teachers) are TWO genuinely different
mechanisms, not compe…
Initial commit: Composer 2.5 Replication Framework — research synthesis
Methodology repo (model type) for an open replication of Cursor's Composer 2.5
(post-trained Kimi K2.5) on any HuggingFace base model.
Contents:
- README.md (HF model card with frontmatter)
- framework/composer-replication-framework.md (master synthesis, 18KB)
- research/ (5 deep-dives by 5 different LLM families, ~107KB total)
- 01-composer-2.5.md (Gemini 3.1 Pro)
- 02-diloco-family.md (DeepSeek V4 Pro)
- 03-monarch-torchforge-openenv.md (GPT-5)
- 04-verl-trl.md (Sonnet 4.6)
- 05-trace-replay-distillation.md (Kimi K2-Thinking)
- docs/METHODOLOGY.md (how the synthesis was produced)
- docs/HF_REPO_LAYOUT.md (planned multi-repo split)
- LICENSE (MIT)
Status: pre-spike. No code, no weights, no datasets yet. Trained variants and
trace datasets will live in separate repos linked via HF Collection.
