Logging & wandb¶

JSONL files¶

retrain writes structured logs to JSONL files in the log_dir directory:

logs/train/
├── metrics.jsonl              # Per-step training metrics
└── emergence/
    ├── steps.jsonl            # Per-step summary for emergence analysis
    └── generations.jsonl      # Individual completions with rewards

metrics.jsonl¶

One JSON object per training step with all metrics:

Field	Description
`step`	Training step index
`condition`	Label like `maxrl+gtpo_sepa`
`loss`	Training loss
`reported_loss`	Raw backend-reported loss value (same as `loss`, explicit for tooling)
`loss_is_placeholder`	`true` when loss is backend placeholder by design
`backend_reports_sync_loss`	Whether backend loss is synchronous optimization loss
`backend_preserves_token_advantages`	Whether backend consumes per-token advantages directly
`mean_reward`	Mean reward across the batch
`correct_rate`	Batch correct rate
`running_correct_rate`	Cumulative correct rate
`sepa_lambda`	Current SEPA pooling strength
`sepa_gate_open`	Whether SEPA correctness gate is open
`num_datums`	Number of datums submitted for training
`max_token_hit_rate`	Fraction of completions that hit max_tokens
`step_time_s`	Wall time for the step
`batch_size`	Current batch size
`group_size`	Current group size
`bp_action`	Back pressure action
`bp_regime`	Back pressure regime
`uncertainty_kind`	Uncertainty variant selected in config (`surprisal`, etc.)
`exec_entropy_mean`	Mean execution-token entropy (GTPO modes)
`exec_entropy_var`	Execution-token entropy variance
`plan_entropy_mean`	Mean planning-token entropy
`plan_entropy_var`	Planning-token entropy variance
`exec_surprisal_mean`	Mean execution-token surprisal (preferred name)
`exec_surprisal_var`	Execution-token surprisal variance (preferred name)
`plan_surprisal_mean`	Mean planning-token surprisal (preferred name)
`plan_surprisal_var`	Planning-token surprisal variance (preferred name)

*_entropy_* keys are kept for backward compatibility and currently mirror *_surprisal_*.

emergence/steps.jsonl¶

Compact per-step summaries for emergence analysis:

Field	Description
`step`	Training step
`mean_reward`	Mean reward
`correct_count`	Number of correct completions
`total_count`	Total completions
`condition`	Condition label

emergence/generations.jsonl¶

Individual completions for qualitative analysis:

Field	Description
`step`	Training step
`prompt`	First 200 chars of the prompt
`completion`	First 500 chars of the completion
`reward`	Reward score
`num_tokens`	Completion length in tokens

wandb¶

Enable wandb by setting wandb_project:

[logging]
wandb_project = "my-project"
wandb_run_name = ""          # defaults to condition label
wandb_entity = ""            # team or user
wandb_group = ""             # for grouping related runs
wandb_tags = ""              # comma-separated

Metric prefixes¶

All wandb metrics use structured prefixes:

Prefix	Metrics
`train/`	`loss`, `rewards/mean_reward`, `rewards/correct_rate`, `rewards/running_correct_rate`, `sepa_lambda`, `sepa_gate_open`, `max_token_hit_rate`, `num_datums`, `step_time_s`, `batch_size`, `group_size`
`train/backend/`	`reports_sync_loss`, `preserves_token_advantages`
`train/` (semantics)	`reported_loss`, `loss_is_placeholder`
`train/entropy/`	`exec_mean`, `exec_var`, `plan_mean`, `plan_var`
`train/surprisal/`	`exec_mean`, `exec_var`, `plan_mean`, `plan_var`
`train/backpressure/`	`action`, `regime`, `p_star`, `sigma`, `kappa`, `utilization`, `throughput`, `warmup`

Run config¶

The wandb run config records all hyperparameters:

advantage_mode, transform_mode, uncertainty_kind, condition
model, lora_rank, lr, batch_size, group_size
max_tokens, temperature, gtpo_beta, hicra_alpha
sepa_steps, sepa_delay_steps, sepa_correct_rate_gate
max_steps, backend, seed

Squeeze metrics¶

When a campaign has a [squeeze] section, a dedicated "squeeze-analysis" wandb run is created after the first training run completes. It logs:

Prefix	Metrics
`squeeze/`	`variance_table` (wandb Table), `mean_variance`, `min_variance`, `max_variance`, `rank`

Summary metrics on the run:

Key	Description
`squeeze/recommended_rank`	Smallest rank meeting the variance threshold
`squeeze/source_rank`	Original training rank
`squeeze/min_variance_retention`	Configured threshold
`squeeze/num_layers`	Number of LoRA layers analyzed

Campaign integration¶

When running campaigns with wandb_project set, each training run gets a structured name, group, and tags for easy comparison in the wandb dashboard. The squeeze-analysis run appears in the same project. See Campaigns.