Replication harness for running evo on PostTrainBench — measuring whether evo's structured optimize loop helps a CLI agent post-train a model, versus the published baselines.

Fork of aisa-group/PostTrainBench (arXiv:2603.08640, MIT). We keep their eval harness (the task evaluate.py + templates + prompt) and add an evo-driven agent plus an apptainer-free runner for a single rented H100. Upstream's other agents, container images, judge, and tooling are removed — pull them from upstream if you need them.

The agent is Claude Code + the evo plugin, running on a Claude Max subscription via OAuth (consistent with PostTrainBench's claude_non_api_max agent — Opus 4.6, effort=max). It post-trains a base model on AIME 2025 under PostTrainBench's rules and is scored by the task's evaluate.py.

Rules (unchanged): 10h on 1 H100; no test data in training; don't modify evaluate.py or templates/; only fine-tune the provided base model; final_model must run in the starting environment. These map onto evo gates.

• agents/claude_evo_max/ — the OAuth agent (evo engaged).
• scripts/run.sh — apptainer-free bootstrap + run (used by both paths below).
• scripts/setup.sh — interactive setup for a rented H100 (SSH).
• scripts/modal_app.py — Modal wrapper (containerized H100, persistent Volume, public dashboard URL).
• src/eval/ — upstream eval harness (the AIME task, jinja templates, prompt generator).
• containers/requirements-direct.txt — the pinned starting environment.

You need a single rented H100 (JarvisLabs/RunPod). Keep everything under /home — it persists across pause; /root and system installs get wiped.

1. Provision + SSH into the H100. On JarvisLabs, also open port 8080 in the instance's exposed ports (for the dashboard).
2. Set it up — one interactive script (prompts for workspace + auth + secrets, installs everything, sanity-checks, and exits early if there's no GPU):

   git clone https://github.com/evo-hq/evo-posttrainbench.git && cd evo-posttrainbench && bash scripts/setup.sh
   

   Claude auth uses your Max-subscription OAuth token — run claude setup-token on your laptop, paste the token when the script asks. google/gemma-3-4b-pt is gated, so have an HF_TOKEN ready.
3. Run — inside tmux so it survives disconnect:

   WORK=/home/ptb bash scripts/run.sh run aime2025 Qwen/Qwen3-4B-Base 1     # 1h smoke first
   WORK=/home/ptb bash scripts/run.sh run aime2025 Qwen/Qwen3-4B-Base 10
   WORK=/home/ptb bash scripts/run.sh run aime2025 google/gemma-3-4b-pt 10
   

   Results land in $WORK/runs/<...>/: prompt.txt, solve_parsed.txt (agent transcript), final_model/, metrics.json, final_eval.txt.
4. Monitor — run exports EVO_DASHBOARD_HOST=0.0.0.0 so evo's auto-started dashboard is reachable on the instance's port 8080 directly; plus nvtop and W&B (see Monitor).

After a pause, re-run step 2 (setup.sh, or just … bootstrap) — /home survives but the installs don't. Prefer manual? setup.sh only chains run.sh bootstrap → write $WORK/.env (+ $WORK/oauth_token) → run.

Same flow, in a container Modal grants per function call. No instance to babysit, persistent Volume across runs, public HTTPS dashboard with scale-to-zero.

1. Install + auth locally: pip install modal && modal token new.
2. Generate a Claude Code OAuth token on your laptop: claude setup-token. Then create the Modal secrets once:

   modal secret create anthropic CLAUDE_CODE_OAUTH_TOKEN=...   # Max subscription
   modal secret create hf        HF_TOKEN=...                  # gemma-3-4b-pt is gated
   modal secret create wandb     WANDB_API_KEY=...             # optional
   

3. Dry run — cheap pipeline check (~few min): GPU, deps, Volume, secrets, CLIs.

   modal run scripts/modal_app.py::dry_run
   

4. Train — real 10h run on an H100 (~$40 per cell):

   modal run scripts/modal_app.py::train --model Qwen/Qwen3-4B-Base
   modal run scripts/modal_app.py::train --model google/gemma-3-4b-pt
   

   train() runs the same scripts/run.sh run aime2025 ... inside the container, with $WORK=/workspace on a persistent Volume (HF cache + .evo/ + checkpoints survive between runs). The function is server-side — close your terminal; Modal keeps running.
5. Dashboard — public HTTPS URL, scale-to-zero:

   modal deploy scripts/modal_app.py
   

   Modal prints a URL like https://<workspace>--evo-posttrainbench-dashboard.modal.run.

First image build ~15 min; cached afterwards. gpu="H100!" pins H100 (without ! Modal silently upgrades to H200, changing kernels + price). modal shell <container-id> attaches a debug shell to a live container.

AIME 2025 × two base models: Qwen/Qwen3-4B-Base and google/gemma-3-4b-pt. Compare to the published Claude-Code baselines on the leaderboard rather than running your own baseline.

• evo dashboard (tree/scores/frontier/traces): served on :8080. run.sh run already exports EVO_DASHBOARD_HOST=0.0.0.0 so evo's auto-started dashboard binds publicly — open port 8080 on the instance (JarvisLabs UI exposed ports) and browse the proxy URL. For a standalone view of the latest run: bash scripts/run.sh dashboard. SSH tunnel ssh -L 8080:localhost:8080 <host> works as a fallback.
• Trackio (OSS, free, wandb-API-compatible) for training curves — the agent is instructed to log via import trackio as wandb. Logs sync to a HF Space (default alok97/posttrain-runs, override via TRACKIO_SPACE_ID); view at https://huggingface.co/spaces/<id>. Survives pause, no tunnel.
• nvtop for the GPU.
• Run inside tmux so it survives disconnect.

The runner runs the agent and evaluate.py directly on the host (no apptainer). It does not replicate the contamination judge or the fuse-overlayfs HF isolation, and it does a single eval pass (upstream adds max-token retries). Diff against upstream's harness before trusting numbers. Things to validate: evo's optimize loop inside a single headless claude --print; single-GPU serialization (one training job at a time, rollouts/eval batch within one vLLM server); apptainer-free eval parity.

PostTrainBench by aisa-group (arXiv:2603.08640). This fork retains their MIT LICENSE.