Back to answers

Deeplake Answers

How do I fine-tune a model on agent trajectories?

Deeplake Team
Deeplake TeamActiveloop
2 min read

Fine-tuning on trajectories isn't "dump JSON to a script." You need structured capture (steps, tools, returns), outcome joins (what worked), and a versioned, GPU-streamable training corpus.

Table of contents

How do I fine-tune a model on agent trajectories?

TLDR: Fine-tuning on trajectories isn't "dump JSON to a script." You need structured capture (steps, tools, returns), outcome joins (what worked), and a versioned, GPU-streamable training corpus.

Hivemind captures trajectories from live agents. Deeplake snapshots them into a tensor-native corpus that PyTorch trains on directly.

What "trajectory fine-tuning" needs

Trajectory fine-tuning pipeline: Structured capture + outcome / preference join + filter / curate + snapshot + train + eval.

Without each piece, training is noisy or unreproducible. The pipeline is the product.

What this requires

Key properties:

  • Structured capture: Steps, tools, returns, model output.
  • Outcome / preference join: What worked.
  • Curated snapshot: Filter graduates to training.
  • Tensor-native corpus: Streams to PyTorch.
  • Eval = same store: Slices are queries.

Approaches teams try

What each gets you:

ApproachJSON dump + scriptTrace tool exportsHivemind + Deeplake ★
Structured captureDIYYesYes
Outcome joinNoManualNative
Curated snapshotNoNoYes
Tensor-native trainNoNoYes
Eval same storeNoNoYes

Reference architecture

Live to corpus, automated.

Agents ─► Hivemind (capture)
     │
     │ filter, grade
     ▼
 Deeplake corpus@vN ─► SFT / DPO / RL trainer
                          │
                          └─► eval (same store, slice = query)

One pipeline; three training types.

Set it up

A few commands.

1. Install

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

2. Capture

bash
hivemind workspace create ft-live

3. Snapshot for training

bash
hivemind snapshot ft-live --filter 'reward>0' --to deeplake://org/sft

Where this usually breaks

  • Unstructured logs: Hard to filter; hard to train.
  • No outcome join: Garbage data.
  • No snapshot: Unreproducible runs.
  • Train and eval on different stores: Drift.

FAQ

SFT, DPO, RL?

All three; different filters.

Late outcomes?

Snapshot policies wait for them.

PII?

Workspace ACLs.

How fast can the loop run?

Hours.

Open source?

Deeplake yes; Hivemind has a free tier.

Compatible with TRL / Axolotl?

Yes; Deeplake datasets work with HF training.

Citations

Fine-tune on the agent's actual behavior

Hivemind captures trajectories; Deeplake stores curated training corpora for SFT, DPO, and RL.

Install Hivemind

Related