[RyanPCo]

Co-Authored-By: Claude Opus 5 (1M context) [email protected]
Claude-Session: https://claude.ai/code/session_01UHDiN8yfERcsiFdz4mXuZi

[RyanPCo]

• configs: declare proprio and actions dims once per HPT model config #634 : 2 dependent PRs (#635 , #636 )
• cache norm stats for data configs to minimize recomputation #633 👈 (View in Graphite)
• import cloud packages lazily to support local training #631 : 1 other dependent PR (#632 )
• cache dataset resolver for faster training initialization #630
• validate episode hashes in data config before training #628
• modify pi camera map to match data #629
• add tests to ensure training works between every model and data vendor #626
• run CI tests on every PR #625
• add env variables for user-specific paths #624
• build: set pyproject and uv as the dependency managers #623
• fix(configs): fix paths for data hydra yaml configs #622
• refactor(algo): delete ACT #627 : 1 other dependent PR (#621 )
• tests: create dedicated test folder with unit and integration tests #620
• add pyright type checker #619
• main

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[RyanPCo]

@greptileai

[greptile-apps]

Confidence Score: 4/5

[Medium risk] Adds caching layer for dataset normalization statistics.

The PR should not merge until chunk-only episode changes invalidate cached normalization statistics.

Findings

1.  Chunk changes reuse stale stats ▶
2.  Malformed cache entries pass validation ▶

Summary

The PR adds a shared, content-keyed normalization-stat cache, wires it into training, and documents and tests the new configuration.

• Cache hits reuse a precomputed stats file; misses compute and write one.
• The episode fingerprint does not detect chunk-only value changes.

Diagram

%%{init: {'theme': 'neutral'}}%%
flowchart LR
  A[Resolve training episodes] --> B[Fingerprint episode metadata]
  B --> C[Hash recipe and code]
  C --> D{Cache hit?}
  D -->|Yes| E[Load norm stats]
  D -->|No| F[Compute norm stats]
  F --> G[Write cache entry]

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