CL-Bench environment streams#

This integration runs database exploration and poker streams using CL-Bench’s environment and MetaWorld’s host seat. MWM maintains environment knowledge between episode blocks. These are environment integrations, not standalone JSONL benchmarks that can be reproduced by installing the framework alone.

Requirements and configuration#

Use a Linux machine with the configured Codex learner, bubblewrap/namespace support, and the framework’s dependencies. Provide CL-Bench and MetaWorld checkouts with their venue-specific dependencies installed. The models are backend references (default GLM 5.3 Flash on Fireworks); the launcher needs the backend’s key variable (FIREWORKS_API_KEY, else the key file <MWM_METAWORLD>/.fireworks). MW_MODEL=<backend>/<model id> binds host and learner to another backend, MW_HOST_MODEL and MW_EVOLVER_MODEL either alone: for a local server, MW_MODEL=vllm/Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 VLLM_BASE_URL=http://127.0.0.1:8000/v1 (Model backends).

Set paths explicitly rather than relying on the launcher’s workstation defaults:

export MWM_CLBENCH=/absolute/path/to/clbench
export MWM_METAWORLD=/absolute/path/to/metaworld
export MW_FRAMEWORK_PYTHON="$PWD/.venv/bin/python"
export MW_RUNS="$PWD/.demo/clbench-runs"

The CL-Bench host environment also uses the benchmark checkout’s .venv/bin/python. For database, the launcher checks that the benchmark prompt contains the accuracy-scoring contract (SCORING_UNLIMITED). Inspect examples/clbench_database/utils/task-info/ and the task runners before starting; a vanilla checkout missing that contract is insufficient.

Prepare and launch#

From the framework repository root:

python examples/clbench_database/utils/prepare.py --clbench "$MWM_CLBENCH"
MW_TAG=mwm-db WM_PORT=3401 MW_ORDERS="0" examples/clbench_database/mwm/launch.sh

For poker, use its own run tag and free port pair:

python examples/clbench_poker/utils/prepare.py --clbench "$MWM_CLBENCH"
MW_TAG=mwm-poker WM_PORT=3411 MW_ORDERS="0" examples/clbench_poker/mwm/launch.sh

These commands make real model calls. Begin with one order. MW_ORDERS="0 1 2 3 4" selects all five. MW_TRAIN_SPLIT can select a prepared prefix split for a smoke run; a prefix is not the full experiment. The launcher uses WM_PORT and WM_PORT+1 for host and learner wire meters.

On local GPUs#

Any server the backends layer knows can stand in for Fireworks. On a machine of L40s (vLLM 0.31), the poker stream ran with two instances of Qwen3.6-35B-A3B-FP8 (two GPUs each), one for the host and one for the learner, declared once in a backends file:

# lab-backends.yaml (HA_BACKENDS points here)
lab-host:    {from: vllm, base_url: http://127.0.0.1:8105/v1, api_key_env: LAB_HOST_KEY, context_window: 131072}
lab-learner: {from: vllm, base_url: http://127.0.0.1:8104/v1, api_key_env: LAB_LEARNER_KEY, context_window: 131072}

vllm serve Qwen/Qwen3.6-35B-A3B-FP8 --tensor-parallel-size 2 --language-model-only --max-model-len 131072 \
    --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder \
    --served-model-name qwen3.6-35b --port 8104            # and again on two other GPUs, --port 8105
HA_BACKENDS=lab-backends.yaml MW_HOST_MODEL=lab-host/qwen3.6-35b MW_EVOLVER_MODEL=lab-learner/qwen3.6-35b \
    MW_TAG=mwm-poker-local WM_PORT=3551 MW_ORDERS=0 examples/clbench_poker/mwm/launch.sh

What was observed on order 0 (2026-10-17), one run per method:

Method	Hands	bb/hand	Note
MWM	120	+0.79	all 22 learner sessions completed; 100 minutes
prompt-only	20	+1.65	stopped at hand 21, see below
MWM, the same first 20 hands	20	+3.20

These are one order with one local model, not a comparison with the results in the example’s README.

The host model matters more than the server. Qwen3-30B-A3B-Instruct-2507 looped as a host: it alternated two wm calls up to the 100-call cap of a hand (3 million input tokens for one hand), and as the database host it wrote its answer as text instead of calling the tool. Qwen3.6 did neither.
A hand whose host takes no action fails twice and is never settled, and the stream cannot go past it: every later hand fails with stream mismatch and is scored 0 (runner_logs/ shows it). The prompt-only run stopped this way at hand 21, after its learner’s instructions told the host to reply with text such as FOLD.
A reasoning model can run one answer past clbench’s request timeout (CLB_REQUEST_TIMEOUT, 600 s); clbench retries it. The prompt-only host’s first block took 27 minutes, most of it in two such answers, before its learner had written any instructions.

Outputs and scoring#

Path under `$MW_RUNS/$MW_TAG/`	Meaning
`<tag>-o<order>/steps.jsonl`	Framework progress and scores for the order
`<tag>-o<order>/checkout/`	Selected adapter and card
`order<order>/run.log`	Training process output
`order<order>/task/evidence/`	Episode evidence
`order<order>/task/outcomes.jsonl`	Poker outcome ledger
`wire_ledger.json`, `meta_wire_ledger.json`	Provider usage captured by the meters

Database uses accuracy scoring with the configured query policy. Poker uses its outcome ledger and bb/hand; do not interpret the framework evaluator’s generic solved_once field as the poker metric.

The example documentation reports roughly a day per order for its model setting. This is an observed planning estimate; verify with a prefix run. No fresh benchmark measurements are implied by this guide.

Preserve the protocol#

Record stream order, model settings, benchmark and MetaWorld revisions, learner block size, and environment reset behavior. Do not shuffle a stream or replace the benchmark protocol with a single final-adapter test.

The native integrations are described in examples/clbench_database/README.md and examples/clbench_poker/README.md; the MWM guide covers the learner contract. Read the produced cards and ledgers for the actual run, including failures and unknown usage.