- register online_ppo train type backed by PPOStrategy: token-level clipped surrogate over GAE advantages plus masked value regression against rollout-pinned returns, with explained-variance metrics
- fold the reference-KL penalty (k3 estimator) into per-token rewards before GAE and pin advantages/returns on RolloutResult so replayed gradient steps optimize fixed targets
- add self-contained ValueModel critic with a zero-initialized value head and backbone warm-started from policy weights; AutoRegressiveLM stays untouched and trunk parity is pinned by tests
- step the critic's own optimizer outside the policy-version lock with the same max_grad_norm clipping as the policy
- persist critic state as value_model.pt/value_optimizer.pt checkpoint extras; resume restores it, fails loudly when missing, and the train.sh completeness check requires the extras for online_ppo configs
- extract shared rollout sequence/logprob helpers from GRPO (behavior unchanged) and add ppo_gamma/ppo_gae_lambda/ppo_vf_coef CLI options
- save an emergency checkpoint even when the signal is handled before the first optimizer step, where optimizer_step == last_ckpt_step used to skip the save entirely (CI race on slow cold-start runners)
- track a saved-this-session flag so interrupted runs always have at least one checkpoint, while normal zero-step completion keeps skipping the save
- shard the Muon Newton-Schulz orthogonalization over the FSDP mesh instead of partial local slices
- import HF checkpoints faithfully: per-head RoPE permutation for q/k projections and qk-norm, qwen3, shared experts, and qk-norm before RoPE (changes numerics for existing use_qk_norm checkpoints)
- make preprocessing and resume self-contained: backfill realigned bucket keys by semantics (masks ones, rest zeros) and snapshot tokenizer files into every checkpoint
- keep RL consistent: sync the offline GRPO old_model each optimizer step and validate online strategies through a public one-off-rollout hook that leaves the replay cache untouched
- fix streaming serving: withhold partial tool-call prefixes with a stream-end flush, stream tool-call arguments from the raw source span, and terminate SSE frames with a blank line
- fix sampling semantics: capture logprobs before top-k/top-p mutate logits in place and detect greedy pipelines polymorphically instead of isinstance bookkeeping
- Write checkpoint payloads to a hidden sibling staging directory, add a versioned checksum manifest, fsync the completed payload, and publish it with an atomic rename
- Republishing an existing step retires the old payload under a hidden sibling name before the atomic rename, so re-runs into the same output directory replace the previous checkpoint instead of raising FileExistsError
- Keep legacy checkpoints loadable, add optional checksum verification, and align metric flushing with checkpoint publication
Co-authored-by: 0z5a <dezhen.lu@student.uni-tuebingen.de>
- rename on_optimizer_step to before_optimizer_step across the callback protocol, built-in callbacks, and trainer call site
- rename on_after_optimizer_step to after_optimizer_step for the symmetric post-step hook
- document the hook pair and the checkpoint save location in developer and training guides
- add a post-step callback hook for checkpoint saves
- preserve updated model, optimizer, and scheduler state
- cover checkpoint ordering with a regression test
- GradientCheckpointingCallback.on_train_begin returns early on empty module list
- previously logged "Gradient checkpointing enabled" even when checkpointing was inactive, misleading profiling
- Propagates MoE load-balancing loss through model outputs
- Logs task, auxiliary, and weighted losses across strategies
- Computes only explicitly requested callback metrics
- Preserves tensor compute_loss API and adds regression tests
- add GradSNRTracker to metric_util.py computing SNR = E[g]^2 / Var(g) via per-parameter EMA moments
- add grad_snr_tracker field to TrainContext (instantiated by default)
- register grad_snr in MetricCallback, update tracker on each optimizer step before metrics are recorded
- add grad_snr to default --metrics in train.py CLI
- Remove separate ValidationCallback, merge into MetricCallback
- Progress bar now tracks optimizer steps instead of micro-steps
- Remove unused log_interval config field and CLI flag
- Fix validation all_reduce: use SUM(loss, count) instead of AVG
- Simplify metric logging: always log every optimizer step
- Add grad_norm display to progress bar
ViperEkura
0z5a