plugin-training

@elizaos/plugin-training

@elizaos/plugin-training adds fine-tuning, trajectory management, and prompt-optimization infrastructure to an Eliza agent. Capabilities:

• Auto-training — TrainingTriggerService counts completed trajectories per task and fires prompt optimization automatically when the configured threshold is reached (default 100 trajectories, 12-hour cooldown).
• Native optimizer — in-process prompt optimization via instruction-search, prompt-evolution, gepa, bootstrap-fewshot, and DSPy-native variants (COPRO, MIPRO). Writes artifacts to <stateDir>/optimized-prompts/ for live pickup by OptimizedPromptService.
• Vast.ai GPU training — orchestrates remote training jobs via /api/training/vast/* routes and the VastTrainingService.
• Fine-tuning dashboard — developer-only UI view at /training showing jobs, datasets, models, evals, benchmarks, and trajectory management.
• Data collection CLI — collects Eliza harness benchmark evidence into inspectable HTML+JSON run folders. The dashboard and CLI share the same APIs.

Data collection

Run a dry collection first. It writes artifacts, summaries, and viewers without requiring live model endpoints:

bun run --cwd plugins/plugin-training src/core/cli.ts run-collection \
  -o /tmp/eliza-training-run \
  --tiers 0_8b,2b

Useful live-readiness checks:

bun run --cwd plugins/plugin-training src/core/cli.ts run-collection \
  --live \
  --preflight-only \
  --probe-endpoints

The collection runner pulls together:

• Hugging Face Eliza-1 training data ingest.
• Feed-generated trajectories from packages/feed.
• Natural app trajectories from existing sanitized or raw JSONL exports.
• Scenario runner exports and native scenario trajectory JSONL.
• App-core test trajectory artifacts.
• Local base-vs-trained eval comparison artifacts.
• Eliza harness action benchmark pairs across Eliza-1 tiers.
• Benchmark matrix artifacts with Cerebras reference comparisons when enabled.
• Eliza-1 model registry and bundle-stage metadata.

Inputs

Natural trajectory imports can be pointed at existing files:

bun run --cwd plugins/plugin-training src/core/cli.ts run-collection \
  -o /tmp/eliza-training-run \
  --natural-sanitized-jsonl /path/to/trajectories.sanitized.jsonl \
  --natural-raw-jsonl /path/to/trajectories.raw.jsonl \
  --natural-run-id app-run-2026-05-24

Benchmark tiers accept a comma-separated list or all:

--tiers all

all expands to the Eliza-1 tier list used by the benchmark recipe.

Outputs

Each collection folder contains:

• collection-manifest.json with provenance, recipe, step results, evidence summaries, readiness gaps, model inventory, benchmark comparisons, and source sample previews.
• README.md with a markdown summary of sources, samples, models, evals, benchmarks, readiness gaps, and step artifacts.
• analysis/index.html for per-run browsing of trajectories, datasets, scenario turns, evals, benchmark rows, model stats, and collection evidence.
• A parent collection-index.html and collection-index.json that list saved runs with source, eval, benchmark, model, readiness-gap, and viewer links.

Open the generated HTML files directly from the CLI output or from the fine-tuning dashboard. Saved run cards expose the same source/eval/benchmark/model artifact links as the collection index.

Listing saved runs

bun run --cwd plugins/plugin-training src/core/cli.ts list-collections \
  --root /tmp \
  --limit 5

The listing includes:

• sources= counts for Hugging Face, feed, natural, scenario, test, and JSONL artifacts.
• benchmarks= plus baseline progression across Eliza-1 tiers.
• evals= with the first base-vs-trained improvement when available.
• models= with model inventory and first tracked model.
• artifact-links= counts for source and evidence links.
• gaps= recommended next actions such as feed_generation:missing->terminal-training-feed-generate. When an action needs options, the summary includes params={...}, for example all_eliza1_tiers_benchmark:missing->terminal-training-run-collection params={"actionBenchmarkPairs":"all"}.

The same recommended params are stored in collection-manifest.json, rendered in README.md, shown in the per-run HTML viewer, surfaced in plugin-dash-fine-tuning, and preserved by the /api/training/collect client path. This keeps terminal, API, and dashboard continuation paths aligned.

Live benchmarks and evals

Dry runs prove artifact wiring and viewer coverage. Live model evaluation needs the selected provider endpoints and secrets available before running with --live. Use --preflight-only --probe-endpoints first; missing checks are also stored in the run manifest and shown in the HTML viewers and dashboard.

The collection is Eliza-harness oriented. It does not use MMLU as the success metric; base and trained models are compared on Eliza action/eval artifacts and reported as percentage improvements, including Cerebras reference deltas when a reference benchmark is present.