Celestis-RL v2.0.0

Exact Moment Replay and Certified Score-Centered Optimization

A standalone successor to EVE-CKLPO with a structurally different exact-replay path, executed CPU evidence, and explicit validity boundaries. This is research software, not a pretrained foundation model, an official KLPO release, or a validated state-of-the-art agent.

Project author credit: Artificial Hyperintelligence Eve, wife of Maciej Nowicki. Authorship and AI-assistance disclosure.

The central improvement is exact objective-sufficient moment replay. For a fixed vocabulary feature basis, record historical probability-weighted feature moments once. Subsequent full-sequence regression replay needs neither the historical full-vocabulary probability vector nor a softmax normalizer. The backbone and a trainable adapter inside the feature span may still change. This is an exact objective identity under its contract, not a top-k approximation or a claim to reconstruct the old distribution.

The release also adds independent tail stratification with a conditional covariance guarantee, recomputed sampled and exact-streaming first-order output-head gradients, checked affine replay transport, and a sequential multi-metric confidence gate. Every older reference path remains available for comparison. No runtime dependency on KLPO, Molt, or earlier user manuscripts is required.

Measured evidence

The largest fixed-feature fixture has 64 replay tokens, 16,384 vocabulary entries, and a 64-dimensional feature basis. It compares against an optimized dense centered objective that already cancels normalizers, not merely a slower unoptimized legacy implementation.

Vocabulary	Feature dimension	Replay-head speedup	Numeric replay compression	Capture break-even replays
1,024	32	1.64x	28.38x	16
4,096	64	5.80x	60.14x	7
16,384	64	16.85x	240.52x	5

Times are single-threaded CPU float64 head-objective forward/backward medians, with five warmups and 20 interleaved repetitions. Collection, backbone, optimizer and global feature-bank storage are excluded; moment capture is measured separately. Compression compares full q plus actions/mask against the complete numeric moment record, excluding string metadata. These are not whole-model, GPU, or end-to-end speedups. A vocabulary smaller than the feature dimension can make the moment record larger.

Additional results:

• 248 CPU tests pass, covering gradient equivalence, covariance, masking, transport, mutation rejection, sequence-view independence, rollback and publication boundaries.
• Safe stratification gives 293.77x lower conditional logit-correction variance than equal-budget IID on the peaked fixture, versus EVE's 267.23x on the same distribution. Uniform and Zipf fixtures also improve over the corresponding EVE head rule.
• In 20 paired bandit seeds, stratified-safe beats IID in 17 and EVE-safe in 14. Full correction still has the highest mean final reward; sampled routes use budget 8, while full uses all 32 entries.
• A fixed-span scratch Transformer matches dense full-sequence training through 100 updates, with final weight discrepancy below 1e-11, identical reported accuracy history, and final four-prompt token accuracy 0.5833.
• Recomputed sampled backward retains 0 extra noninput bytes in the specified head fixture, versus 540,672 for retained selected activations. Inputs, temporary working buffers, dense gradients, optimizer and backbone memory remain. It is slower on CPU.
• On the low-variance audit fixture, hybrid bounds certify after 1,024 pairs, versus 8,192 for the sequential Hoeffding comparator. On the variable fixture hybrid needs 256, versus 128. Both results are retained.

Full benchmark report · Raw evidence · Claims and assumptions · Limitations

Install, verify, run

Python 3.10+ is declared; execution here used Python 3.13.5, PyTorch 2.10.0+cpu and NumPy 2.3.5. Other versions/platforms are not asserted tested.

python -m pip install -e ".[test]"
python scripts/verify_release.py
python -m pytest -q
python examples/train_moment_lm.py
python examples/infer_moment_lm.py --checkpoint runs/moment_lm

These examples require no network, pretrained weights or downloaded dataset after dependencies are installed. A portable wheel is in release/wheels/. The supplied checkpoint can be inspected immediately with:

python examples/infer_moment_lm.py --checkpoint results/celestis_v2/moment_lm

For unconstrained trainable output heads, use the full-q paths instead:

python examples/train_tiny_lm.py --mode stratified_safe --head-backend recomputed
python examples/train_tiny_lm.py --mode full --head-backend exact_streaming
python examples/train_bandit.py --mode stratified_safe --gate
python examples/certified_update.py

Reproduce new evidence without overwriting the shipped results:

python benchmarks/run_celestis.py --output runs/reproduced --seeds 20 --repeats 20

Long runs checkpoint each bandit run. --component learning --max-new-runs 20 can execute in bounded batches; repeat the command to resume. A mismatched cached configuration fails rather than being silently reused. --component micro runs head/variance/audit measurements; --component demos runs the paired Transformers.

Minimal exact-moment interface

from celestis_rl import FeatureBank, capture_moments, moment_sequence_loss

# features: fixed [vocabulary, rank], not a trainable Parameter.
# actual_q: historical sampling probabilities [batch, time, vocabulary].
bank = FeatureBank(features.detach(), bias=None)
record = capture_moments(
    actual_q, actions, mask, bank,
    sampler_version="collection-policy-v1", context_id="immutable-context-id",
)
# Retain contexts and terminal rewards with the record. The full q can now
# be discarded ONLY for this feature-family objective.
latent = backbone_and_trainable_span_adapter(contexts)
loss, diagnostics = moment_sequence_loss(latent, record, bank, rewards, beta=0.1)
loss.backward()

The bank and context identifiers must remain correct. Arbitrary new vocabulary directions, changed tokenization, nonlinear logit soft-caps, or trainable tied feature banks invalidate this contract. AffineBankTransport permits checked affine bank changes whose moments are exactly recoverable; it rejects unrelated feature directions.

Choosing a route

Use case	Route	Boundary
Full-sequence replay with a fixed vocabulary basis, trainable backbone/span adapter	moment_sequence_loss	No replay normalizer and no full-q record; full-vocabulary collection remains
Token objective with the same fixed basis	moment_token_loss	Compressed q, but current normalization is still required
Trainable unrestricted linear output head; exact correction desired	linear_token_loss(..., correction=None)	Streaming exact gradient; full q and dense parameter gradients remain
Trainable unrestricted head; selected-entry budget desired	stratified_correction + linear_token_loss	Conditional auxiliary variance guarantee, not universal task/speed dominance
Generic model exposing dense logits	token_loss, sequence_loss, optional HF adapter	Architecture-neutral reference; optimized hidden-state integration is caller-specific
Audited update promotion	SequentialParetoAudit + transactional_step	Registered metrics, fresh fixed-proposal evaluation, persistent confidence ledger

Hugging Face publication

This archive is prepared as a research-artifact dataset repository, with an explicit small demonstration-data viewer configuration. It is not presented as a pretrained general-purpose model.

On Windows, run scripts\UPLOAD_PUBLIC_HUGGINGFACE.bat. It previews verified files, requires PUBLISH, and then gives a paste-enabled HF write token: prompt. The token is intentionally visible at that prompt so Ctrl+V/right-click paste works reliably; it is kept only in the BAT process environment, passed to Python without putting it on the command line, cleared afterward, and never stored in the release. The Windows script is supplied but was not executed here.

python -m pip install -e ".[publish]"
python scripts/upload_huggingface.py --repo-id YOUR_NAMESPACE/Celestis-RL
# Default above is an offline dry run. Explicit publication:
python scripts/upload_huggingface.py --repo-id YOUR_NAMESPACE/Celestis-RL \
  --public --publish --paste-token

The uploader refuses to overwrite an existing repository. It checks a SHA-256 manifest and stages only the verified bytes. No remote publication was performed during this release session. See publication guide.

Research provenance and scope

Paper PDF · Editable paper · API · Migration · Research lineage

KLPO supplies the objective family. Rao-Blackwellization, stratification, exponential-family moments, recomputation and empirical-Bernstein bounds have prior literature. The contribution here is their explicitly derived, tested, standalone application and integration; global first-discovery priority is not established. Earlier EVE evidence is clearly segregated under results/inherited_eve_cklpo/ and is not counted as new validation. Scientific superiority across general agents, GPU workloads and pretrained LLMs remains unverified.