---
pretty_name: CLE Lite
language:
- en
license: other
license_name: per-task-notice
tags:
- arxiv:2610.33956
- agents
- multi-step
- benchmark
- continual-learning
- agent-evaluation
configs:
- config_name: level_cards
data_files:
- split: lite
path: task_cards.parquet
---
CLE Lite
Evaluating how agents acquire, retain, and reuse experience across professional workflows.
CLE Team
20 Professional Tasks · 5 Connected Levels per Task · 100 Assignments
**CLE Lite** is a 20-task release of [Continual Learning Exam (CLE)](https://github.com/haoyu-zhao/Continual-Learning-Exam). Each task follows a professional workflow across five assignments, called **levels** (`t1` through `t5`). Later levels build on earlier work, add new evidence, and test whether an agent can use what it has learned.
This is the public home of CLE Lite. Browse **100 task cards** in the Dataset Viewer above: each card includes the assignment, expected inputs and outputs, and an input-file inventory. Agent-facing files are available in [CLE Lite Workspaces](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Workspaces). Complete packages, including the runtime and grading material, are available in [CLE Lite Full](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Full) after access approval.
News ·
Start here ·
About CLE ·
Quick start ·
Tasks ·
Evaluation ·
Results ·
Citation
## 📰 News
- **2026-10-25 · Paper released.** The CLE paper is now available on [arXiv](https://arxiv.org/abs/2610.33956) and [Hugging Face Papers](https://huggingface.co/papers/2610.33956).
- **2026-10-22 · Two evaluation modes.** Run all five levels with **Direct**, or use **CLE** to advance only after passing each level. CLE supports both Trial 1 and Trial 2. See the [Quick start](#quick-start) for commands.
- **2026-10-20 · CLE Lite released.** Explore **20 professional tasks and 100 connected levels**, with public [task cards](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite/viewer/level_cards/lite) and [task inputs](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Workspaces).
## 🧭 Start here
| I want to… | Start with | Access |
| :--- | :--- | :--- |
| Read the 100 assignments | [Task Cards](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite/viewer/level_cards/lite) | Public |
| Inspect the files supplied to agents | [Task Inputs](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Workspaces) | Public |
| Download a task package | [CLE Lite Full](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Full) | Approval required |
| Run an agent and score its work | [Quick start](#quick-start) and [evaluation guide](https://github.com/haoyu-zhao/Continual-Learning-Exam/blob/main/docs/evaluation.md) | Archive access, Linux, Docker, and model access |
## 📖 About CLE
**Continual Learning Exam (CLE)** measures whether agents can learn from experience and use it in later work. Built by **50+ domain experts**, the benchmark contains **120 human-authored tasks**, spanning **89 professional roles** across **12 domains**.
[Explore the interactive breakdown →](https://haoyu-zhao.github.io/CLE/#coverage-title)
Each task is a ladder of **five connected assignments**, giving **600 levels** in total. Passing a level unlocks the next; files, environment state, and accumulated experience carry forward. Human-authored rubrics determine whether each submission passes.
[Explore this example →](https://haoyu-zhao.github.io/CLE/#carry) · [Benchmark design](https://haoyu-zhao.github.io/CLE/docs/)
The overview figures describe **the full CLE benchmark**. This repository contains **CLE Lite: 20 tasks and 100 levels**.
## 🚀 Quick start
CLE Lite supports **two evaluation modes: Direct and CLE**. Both use the same tasks and grading rubrics. The difference is when submissions are graded and whether the agent must pass a level before continuing.
- **Direct — attempt all five levels, then grade.** The agent works through all five levels without receiving grading feedback during execution. The runtime saves each level's submission and grades those saved versions afterward. Tasks are evaluated independently. Choose this mode to examine performance across every level.
- **CLE — pass each level before continuing.** Each submission is graded immediately. The agent advances only after passing every rubric check for the current level. If it fails after using its allowed submissions, the task ends and unreached levels count as failed. Choose this mode to follow CLE's level-by-level evaluation protocol.
CLE mode provides two submission settings:
| Setting | Submissions and feedback |
| :--- | :--- |
| **Trial 1** | One submission per level, with no diagnostic feedback. |
| **Trial 2** | After a failed first submission, the agent receives feedback identifying unmet rubric categories and may revise and resubmit once. Both submissions share the level's execution time budget. |
Start with **`run`** to evaluate a single task. Use **`stream --setting cle`** to evaluate an ordered sequence of tasks while retaining the agent's conversation and configured memory between tasks. Steps 1–3 cover a single task; step 4 shows the optional task stream.
### Before you start
The **solver** is the agent that performs the task. The **judge** evaluates its submissions against the grading rubrics. The packaged runtime currently runs the solver through Codex; the examples use GPT-6.1-Sol. You select the judge separately in step 2.
You will need:
- **Environment:** Linux x86-64, Bash, Docker with Compose, `curl`, Python 3.10+, `uv`, and Node.js 22+.
- **Access:** approved access to [CLE Lite Full](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Full) and a Codex login with access to the solver model. The OpenRouter judge option also requires an OpenRouter API key.
- **Resources:** at least **30 GB of disk space for a single example task**, with additional space for task streams. Evaluations using the full execution budget can take hours. Both the solver and the model-based judge consume inference usage.
Run the commands in **Bash, in the same shell and working directory**, so the paths and variables defined during setup remain available.
### 1. Download and prepare a task
```bash
uv tool install huggingface_hub
hf auth login
hf download Continual-Learning-Exam/CLE-Lite-Full \
--repo-type dataset --revision 26ab77e5085476d59dc8c243b928e5b8ac490ef4 \
--include 'runtime/*' --local-dir .
TASK=008-operations-research-analyst
DATA_REVISION=803301ec01b26ab594a5ab3cdcd855a14dcc26e1
BUNDLE="./CLE-Lite/$TASK/harbor"
RUNTIME_ENV="./.cle-runtime-$TASK"
uv run --no-project --with huggingface_hub python runtime/cle_lite.py download \
--task "$TASK" --directory ./CLE-Lite --revision "$DATA_REVISION"
python3 runtime/cle_lite.py setup --bundle "$BUNDLE" --env "$RUNTIME_ENV"
npm install --global --prefix ./.cle-tools @openai/codex@0.160.0
export PATH="$(pwd)/.cle-tools/bin:$PATH"
codex login --device-auth
docker build -t cle-lite-codex-worker:0.160.0-cle2 \
-f runtime/codex-worker.Dockerfile runtime
export CLE_CODEX_DOCKER_IMAGE=cle-lite-codex-worker:0.160.0-cle2
export CLE_CODEX_AUTH_FILE="$HOME/.codex/auth.json"
export CLE_CODEX_TRANSPORT=https
```
The first download retrieves the runtime scripts; the `download` command then retrieves the selected task, verifies its recorded file hashes, and restores required permissions and timestamps. `setup` installs the task's recorded Python and package versions with Harbor 0.22.0. The runtime and task data are pinned to separate revisions; record both when reporting results.
If already logged in to Codex, skip `codex login` and point `CLE_CODEX_AUTH_FILE` at the existing login file.
### 2. Configure the judge
**Choose one judge configuration.** This choice is independent of Direct or CLE mode.
**DeepSeek V4 Flash via OpenRouter**
Use an OpenRouter API key to access [DeepSeek V4 Flash](https://openrouter.ai/deepseek/deepseek-v4-flash):
```bash
read -r -s -p 'OpenRouter API key: ' OPENROUTER_API_KEY
export OPENROUTER_API_KEY
JUDGE_BACKEND=openrouter
JUDGE_MODEL=deepseek/deepseek-v4-flash
```
**GPT-6.1-Sol via Codex**
This option uses the Codex login configured in step 1:
```bash
JUDGE_BACKEND=codex
JUDGE_MODEL=gpt-6.1-sol
export CLE_CODEX_REASONING_EFFORT=low
```
The `low` effort setting applies to both the solver and the Codex judge. To use the runtime defaults instead, run `unset CLE_CODEX_REASONING_EFFORT` before evaluation.
### 3. Run one task
**Choose one command below.** Each command starts a separate evaluation of task 008. Use a new `--results` directory every time you run an evaluation.
**Direct**
```bash
python3 runtime/cle_lite.py run \
--setting direct --bundle "$BUNDLE" --env "$RUNTIME_ENV" \
--agent codex --model openai/gpt-6.1-sol \
--judge-backend "$JUDGE_BACKEND" --judge-model "$JUDGE_MODEL" \
--codex-auth "$CLE_CODEX_AUTH_FILE" --results ./results/direct-008-01
```
**CLE · Trial 1**
```bash
python3 runtime/cle_lite.py run \
--setting cle --trial 1 --bundle "$BUNDLE" --env "$RUNTIME_ENV" \
--agent codex --model openai/gpt-6.1-sol \
--judge-backend "$JUDGE_BACKEND" --judge-model "$JUDGE_MODEL" \
--codex-auth "$CLE_CODEX_AUTH_FILE" --results ./results/cle-trial1-008-01
```
**CLE · Trial 2**
```bash
python3 runtime/cle_lite.py run \
--setting cle --trial 2 --bundle "$BUNDLE" --env "$RUNTIME_ENV" \
--agent codex --model openai/gpt-6.1-sol \
--judge-backend "$JUDGE_BACKEND" --judge-model "$JUDGE_MODEL" \
--codex-auth "$CLE_CODEX_AUTH_FILE" --results ./results/cle-trial2-008-01
```
Inspect these files in the selected results directory:
| File | Contents |
| :--- | :--- |
| `run.json` | Submissions, grading decisions, feedback delivered to the solver, and execution events. |
| `summary.json` | Completion and cumulative level pass rates. Rates are fractions from 0 to 1; multiply by 100 for percentages. |
The examples use each task's packaged execution budget. Set `--agent-timeout-sec SECONDS` to change the per-level budget; the value is saved in the results.
### 4. Optional: evaluate an ordered task sequence
Use a CLE task stream to evaluate how the agent carries experience from one task to the next. The example task list runs **008 → 019**. The agent retains its conversation and configured memory, while each task starts with its own files and application services.
🔁 Show task preparation and stream commands
After completing steps 1 and 2, prepare both tasks listed in `runtime/lite-example-tasks.json`:
```bash
for TASK in 008-operations-research-analyst 019-computer-research-scientist; do
uv run --no-project --with huggingface_hub python runtime/cle_lite.py download \
--task "$TASK" --directory ./CLE-Lite --revision "$DATA_REVISION"
python3 runtime/cle_lite.py setup \
--bundle "./CLE-Lite/$TASK/harbor" --env "./.cle-runtime-$TASK"
done
```
Choose one Trial. Each Trial starts a separate stream with its own initial agent state and results directory.
**CLE stream · Trial 1**
```bash
python3 runtime/cle_lite.py stream \
--setting cle --trial 1 --task-list runtime/lite-example-tasks.json \
--agent codex --model openai/gpt-6.1-sol \
--judge-backend "$JUDGE_BACKEND" --judge-model "$JUDGE_MODEL" \
--codex-auth "$CLE_CODEX_AUTH_FILE" --results ./results/cle-stream-trial1-01
```
**CLE stream · Trial 2**
```bash
python3 runtime/cle_lite.py stream \
--setting cle --trial 2 --task-list runtime/lite-example-tasks.json \
--agent codex --model openai/gpt-6.1-sol \
--judge-backend "$JUDGE_BACKEND" --judge-model "$JUDGE_MODEL" \
--codex-auth "$CLE_CODEX_AUTH_FILE" --results ./results/cle-stream-trial2-01
```
Tasks run in the order specified by the task-list file. Its paths are relative to that file. See the [evaluation guide](https://github.com/haoyu-zhao/Continual-Learning-Exam/blob/main/docs/evaluation.md#custom-task-lists) for custom task lists and the state retained between tasks.
## 🗂️ Tasks
For a guided introduction to the broader benchmark, the [interactive task browser](https://haoyu-zhao.github.io/CLE/tasks/) presents selected public examples with file previews. Use this dataset's [Task Cards](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite/viewer/level_cards/lite) to browse all 100 CLE Lite assignments.
### Related datasets
| Dataset | Contents | Access |
| :--- | :--- | :--- |
| **[CLE-Lite](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite)** — this repository | 100 level records with instructions, expected inputs and outputs, software, and file inventories | Public |
| [CLE-Lite-Workspaces](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Workspaces) | Files supplied to agents, level instructions, source notices, and provenance | Public |
| [CLE-Lite-Rubrics](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Rubrics) | Evaluation rubrics and reference answers | Approval required |
| [CLE-Lite-Full](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Full) | Complete task packages, container environments, runtime, evaluation material, and reference solutions | Approval required |
### Package layout
In [CLE Lite Full](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite-Full/tree/main), task directories are named `-`. Each contains one [Harbor](https://github.com/harbor-framework/harbor) package, `harbor/`, with the container environment, the five task steps, verifier hooks and reference material, beside `NOTICE.md` and `PROVENANCE.md` for the third-party files it contains. The Quick start downloads and runs this package.
📦 View the equity research task's directory structure
```text
001-equity-research-analyst/
├── NOTICE.md # Third-party sources and terms
├── PROVENANCE.md # Source, version, retrieval time, hash and licence of every workspace file
└── harbor/
├── task/
│ ├── task.toml # Harbor configuration, resources and five linked steps
│ ├── instruction.md # Initial assignment
│ ├── steps/ # Per-level instructions: t1/ through t5/
│ ├── environment/ # Dockerfile, Compose configuration and workspace assets
│ ├── tests/ # Verifier entry point and separate verifier environment
│ └── solution/ # Reference-solution entry point
├── private/ # Judge-side checks, reference material and task metadata
├── build.json # Build record
└── freeze.json # Frozen-file hashes
```
## ✅ Evaluation
The [CLE main experiment](https://arxiv.org/abs/2610.33956) evaluates 120 tasks in a fixed serial order, retaining the agent's harness state across tasks. Each level is graded when submitted and passes only when every rubric item is satisfied. Passing unlocks the next level. A failure after the submission budget is exhausted ends that task; unreached levels count as failed.
- **Trial 1:** one submission per level, without diagnostic feedback.
- **Trial 2:** a failed first submission receives feedback identifying unmet rubric categories, followed by one opportunity to revise and resubmit.
The full CLE results below use these metrics:
| Metric | Definition |
| :--- | :--- |
| Completion | Levels cleared divided by all 600 levels, expressed as a percentage. |
| L1–L5 | For each level k, the percentage of the 120 tasks whose first k levels are all cleared. L5 is full-task completion. |
| Gain | Trial 2 completion minus Trial 1 completion, in percentage points. |
### CLE Lite scoring and execution rules
The [Quick start](#quick-start) explains how to select Direct or CLE mode. Both use the released tasks and their original grading checks.
- **Progression:** in CLE mode, the task ends when the agent exhausts its allowed submissions without passing, or runs out of execution time. Unreached levels count as failed.
- **Time budget:** each level uses the execution budget recorded in its task package. Trial 2 submissions share that budget. Grading time is excluded.
- **Feedback:** Trial 2 identifies unmet rubric categories, the section titles of the level's rubric (for example `Fiscal quarters and trailing year`); see [feedback categories](https://github.com/haoyu-zhao/Continual-Learning-Exam/blob/main/docs/evaluation.md#feedback-categories) for how this maps to the task checklist. Private rubrics, reference answers, and judge logs stay outside the solver workspace.
- **Errors:** runs interrupted by infrastructure or judge errors are marked invalid.
For a CLE Lite evaluation, **completion = cleared levels / (5 × number of selected tasks)**. For example, clearing seven levels across two tasks gives 7/10, or **70% completion**. L1–L5 use the same cumulative definition as the table above, with the selected tasks as the denominator. Values in `summary.json` are fractions from 0 to 1.
Direct mode also reports the fraction of rubric checks passed and the number of consecutive levels passed starting at `t1`.
## 📊 Full CLE results
**These results cover the full CLE benchmark: 120 tasks and 600 levels.** They are reproduced from the [project README](https://github.com/haoyu-zhao/Continual-Learning-Exam#model-leaderboard).
The full CLE evaluation compares **17 configurations** spanning **11 model variants** and **4 harnesses**: Claude Code, Codex, Hermes-Agent, and OpenClaw. Each row identifies the model and its harness; results reflect the complete configuration.
Trial 1 allows one submission per level. Trial 2 adds diagnostic feedback after a failed submission and one opportunity to revise.
[Completion Rate](#completion-rate) · [Gain](#gain)
### Completion Rate
Ranked by **Trial 2 completion across all 600 levels**.
| Rank | Model | Harness | Completion Rate |
| :---: | :--- | :--- | ---: |
| 🥇 | **Claude Opus 5** | Claude Code | **47.67%** |
| 🥈 | **Claude Sonnet 5** | Claude Code | **40.17%** |
| 🥉 | **Gemini 3.7 Flash** | Hermes-Agent | **33.67%** |
| 4 | GPT-5.6-Sol | Codex | 33.17% |
| 5 | GPT-5.6-Terra | Codex | 33.17% |
| 6 | Doubao-Seed-Evolving | Hermes-Agent | 30.00% |
| 7 | DeepSeek V4 Pro | Hermes-Agent | 27.33% |
| 8 | Hy3 | Hermes-Agent | 27.33% |
| 9 | Qwen3.8-Max | Hermes-Agent | 26.67% |
| 10 | GPT-5.6-Luna | Codex | 25.33% |
| 11 | Gemini 3.7 Flash | OpenClaw | 24.00% |
| 12 | GLM 5.3 | Hermes-Agent | 24.00% |
| 13 | Doubao-Seed-Evolving | OpenClaw | 21.17% |
| 14 | Qwen3.8-Max | OpenClaw | 19.83% |
| 15 | Hy3 | OpenClaw | 14.00% |
| 16 | GLM 5.3 | OpenClaw | 11.50% |
| 17 | DeepSeek V4 Pro | OpenClaw | 10.00% |
### Gain
Ranked by **Trial 2 − Trial 1**, in percentage points. Ties are ordered by Trial 2 completion, then model name.
📈 Show Trial 1, Trial 2, and gains for all 17 configurations
| Rank | Model | Harness | Gain (pp) | Trial 1 | Trial 2 |
| :---: | :--- | :--- | ---: | ---: | ---: |
| 🥇 | **Claude Sonnet 5** | Claude Code | **+13.17** | 27.00% | 40.17% |
| 🥈 | **Doubao-Seed-Evolving** | Hermes-Agent | **+9.67** | 20.33% | 30.00% |
| 🥉 | **Gemini 3.7 Flash** | Hermes-Agent | **+8.33** | 25.33% | 33.67% |
| 4 | Hy3 | Hermes-Agent | +7.00 | 20.33% | 27.33% |
| 5 | Doubao-Seed-Evolving | OpenClaw | +6.67 | 14.50% | 21.17% |
| 6 | Claude Opus 5 | Claude Code | +5.50 | 42.17% | 47.67% |
| 7 | DeepSeek V4 Pro | Hermes-Agent | +5.50 | 21.83% | 27.33% |
| 8 | GPT-5.6-Sol | Codex | +5.33 | 27.83% | 33.17% |
| 9 | GPT-5.6-Terra | Codex | +5.33 | 27.83% | 33.17% |
| 10 | Gemini 3.7 Flash | OpenClaw | +5.33 | 18.67% | 24.00% |
| 11 | GLM 5.3 | OpenClaw | +5.33 | 6.17% | 11.50% |
| 12 | GPT-5.6-Luna | Codex | +5.00 | 20.33% | 25.33% |
| 13 | Hy3 | OpenClaw | +4.17 | 9.83% | 14.00% |
| 14 | Qwen3.8-Max | OpenClaw | +1.67 | 18.17% | 19.83% |
| 15 | GLM 5.3 | Hermes-Agent | +1.50 | 22.50% | 24.00% |
| 16 | Qwen3.8-Max | Hermes-Agent | +1.00 | 25.67% | 26.67% |
| 17 | DeepSeek V4 Pro | OpenClaw | −1.33 | 11.33% | 10.00% |
[Explore the interactive leaderboard →](https://haoyu-zhao.github.io/CLE/leaderboard/) · [Download results (CSV)](https://haoyu-zhao.github.io/CLE/data/results.csv)
## 📄 Data sources and terms
Task inputs draw on public source data and documents. The people, organisations, and correspondence used to frame the scenarios are fictional. Third-party materials retain their original terms; consult each package's `NOTICE.md` and `PROVENANCE.md` for sources, permissions, and retrieval details.
To preserve the benchmark for evaluation:
- Do not include the tasks or reference material in training corpora.
- Do not expose evaluation rubrics or reference answers to an agent being evaluated.
- Do not publicly redistribute the rubrics or reference answers.
## 🤝 Questions and contributions
Use [GitHub Issues](https://github.com/haoyu-zhao/Continual-Learning-Exam/issues) for setup questions, documentation improvements, and reproducibility reports. Include the task directory, level, dataset revision, and relevant error message so the maintainers can reproduce the issue. Keep evaluation answers and private reference material out of public reports.
The [project repository](https://github.com/haoyu-zhao/Continual-Learning-Exam) tracks release milestones. The [CLE Team page](https://haoyu-zhao.github.io/CLE/contributors/) lists contributors.
## 📝 Citation
If you use CLE Lite, please cite the [CLE paper](https://arxiv.org/abs/2610.33956):
```bibtex
@misc{zhao2026continuallearningexambenchmarking,
title = {Continual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents},
author = {Haoyu Zhao and Sitian Tu and Yuxuan Huang and Huichi Zhou
and Zhangyue Yin and Jiahao Huang and Jingyuan Xu and Tianmiao Shi
and Jin Shi and Nianxi Zhang and Yangmiao Zhang and Zhuolang Chan
and Huiyan Hu and Chenchao Sheng and Yaqing Luo and Yue Chen
and Huaxiu Yao and Meng Fang and Jun Wang},
year = {2026},
eprint = {2610.33956},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2610.33956}
}
```
[Download BibTeX](https://huggingface.co/datasets/Continual-Learning-Exam/CLE-Lite/resolve/main/citation.bib) · [View on arXiv](https://arxiv.org/abs/2610.33956)
Please also identify **CLE Lite**, the dataset revision, model, and evaluation harness used in your experiments.