Every training segment now saves checkpoint_NNNNNNN.zip so the full training history is preserved on disk. No checkpoint is ever overwritten. model.zip still updated for crash recovery. After a 90k-step run with 13 segments you now have: checkpoint_0006851.zip <- step 6,851 checkpoint_0013702.zip <- step 13,702 ... checkpoint_0090000.zip <- step 90,000 best_model.zip <- highest scoring segment (reloaded at end) model.zip <- latest weights (crash recovery) This means we can NEVER again lose a good mid-training model. If the model was driving at step 30k, checkpoint_0030000.zip exists. Agent: pi Tests: 102 passed Tests-Added: 0 TypeScript: N/A |
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|---|---|---|
| .harness | ||
| agent | ||
| docs | ||
| tests | ||
| .gitignore | ||
| AGENT.md | ||
| DECISIONS.md | ||
| IMPLEMENTATION_PLAN.md | ||
| PROJECT-KICKOFF.md | ||
| PROJECT-SPEC.md | ||
| README.md | ||
| create_gitea_repo.py | ||
| ralph-loop.sh | ||
README.md
donkeycar-rl-autoresearch
Purpose
Status
- Scaffolded with the agent harness
- Spec not filled yet
Runbook
- Fill PROJECT-SPEC.md
- Create IMPLEMENTATION_PLAN.md from the spec
- Start the implementation loop