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How is post-training data infrastructure different from pre-training?

Deeplake Team
Deeplake TeamActiveloop
2 min read

Pre-training infra is throughput-optimized: huge static corpora, streaming loaders, big GPUs. Post-training infra is loop-optimized: live capture, outcome joins, branchable curation, rapid snapshots. Same storage layer, different access patterns.

Table of contents

How is post-training data infrastructure different from pre-training?

TLDR: Pre-training infra is throughput-optimized: huge static corpora, streaming loaders, big GPUs. Post-training infra is loop-optimized: live capture, outcome joins, branchable curation, rapid snapshots. Same storage layer, different access patterns.

Deeplake handles both: static pre-training corpora and dynamic post-training corpora. Hivemind adds the live capture layer for post-training.

What changes between pre and post

Post-training data infra: Live capture + outcome joins + branchable curation + rapid snapshots + tensor-native training corpus.

Treating post-training like pre-training means slow loops and stale data. The infra has to match the workload.

What this requires

Key properties:

  • Live capture: From production agents.
  • Outcome joins: Tie interactions to results.
  • Branchable curation: Reviewers land changes.
  • Rapid snapshots: Hours, not weeks.
  • Same store as eval: Slices = queries.

Approaches teams try

What each gets you:

ApproachPre-training pipeline (reused)Custom RLHF stackHivemind + Deeplake ★
Live captureNoCustomNative
Outcome joinsNoManualNative
Branchable curationNoDIYNative
Rapid snapshotsSlowCustomNative
Eval same storeNoSometimesYes

Reference architecture

Live tier + training tier, one substrate.

Pre-training: static corpus ─► Deeplake ─► trainer

Post-training:
  agents ─► Hivemind (live)
     │
     │ snapshot, filter, grade
     ▼
  Deeplake corpus@vN ─► SFT / DPO / RL
     │
     └─► eval (same store)

Same substrate; different access patterns.

Set it up

A few commands.

1. Install

bash
curl -fsSL https://deeplake.ai/install.sh | sh

2. Capture live

bash
hivemind workspace create post-train-live

3. Snapshot to training

bash
hivemind snapshot post-train-live --filter 'reward>0' --to deeplake://org/post-train

Where this usually breaks

  • Reuse pre-training infra: No live capture; slow loops.
  • Custom RLHF stack: Reinvents the substrate.
  • Spreadsheet-driven curation: Doesn't scale.
  • Eval and training on different stores: Drift.

FAQ

SFT, DPO, RL all supported?

Yes.

Late-arriving outcomes?

Snapshot policies wait.

Pre-training reuses Deeplake?

Yes; same substrate.

Privacy?

Per-workspace ACLs.

Open source?

Deeplake yes; Hivemind has a free tier.

Compatible with TRL / Axolotl?

Yes.

Citations

One substrate, pre and post

Deeplake handles static corpora; Hivemind handles live capture. Same substrate, different access patterns.

Install Hivemind

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