Datasets:
Modalities:
Text
Formats:
json
Languages:
English
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< 1K
Tags:
reinforcement-learning
policy-optimization
klpo
exact-moment-replay
score-centering
stratified-sampling
License:
| import numpy as np | |
| import pytest | |
| import torch | |
| from celestis_rl.training import TrainConfig, run_bandit, run_tiny_lm, load_tiny_weights | |
| def test_small_bandit_reproducible_and_improves(): | |
| cfg = TrainConfig(steps=30, batch_size=16, replay_capacity=64, learning_rate=0.1) | |
| a, b = run_bandit(cfg, vocabulary=8), run_bandit(cfg, vocabulary=8) | |
| assert a["history"] == b["history"] | |
| assert a["final_reward"] > a["initial_reward"] | |
| def test_exact_gate_history_non_decreasing(): | |
| r = run_bandit(TrainConfig(steps=25, batch_size=8, learning_rate=0.4), vocabulary=8, gate=True) | |
| x = [h["exact_expected_reward"] for h in r["history"]] | |
| assert np.all(np.diff(x) >= 0) | |
| def test_tiny_train_and_safe_numeric_checkpoint(tmp_path): | |
| cfg = TrainConfig(steps=2, batch_size=4, learning_rate=0.002) | |
| r = run_tiny_lm(cfg, output=tmp_path) | |
| model = load_tiny_weights(tmp_path) | |
| assert torch.isfinite(model(torch.tensor([[2, 3, 3]]))).all() | |
| assert 0 <= r["final_greedy_accuracy"] <= 1 | |
| def test_dense_selected_training_agree_short_run(): | |
| cfg = TrainConfig(steps=3, batch_size=4, learning_rate=0.002) | |
| a = run_tiny_lm(cfg, selected_backward=True) | |
| b = run_tiny_lm(cfg, selected_backward=False) | |
| assert a["history"] == b["history"] | |
| def test_invalid_config(kw): | |
| with pytest.raises(ValueError): TrainConfig(**kw).validate() | |