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docs: add Supported Patterns section to README #1155

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48cc784
fix #1074: handle dict-based word_list in TextArenaEnv.ta_to_hf()
vominh1919 Apr 16, 2026
f5b4027
docs: add Supported Patterns section to README (#1078)
vominh1919 Apr 16, 2026
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23 changes: 23 additions & 0 deletions README.md
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Expand Up @@ -174,6 +174,29 @@ prime eval run primeintellect/math-python

**[FAQs](docs/faqs.md)** - Other frequently asked questions.

## Supported Patterns

Verifiers supports a wide range of RL environment design patterns. Here's a quick reference:

| Pattern | Support | Documentation |
|---------|---------|---------------|
| **Single-turn Q&A** | `SingleTurnEnv` | [Environments](docs/environments.md) |
| **Multi-turn interactions** | `MultiTurnEnv` | [Environments: Multi-Turn](docs/environments.md#custom-multi-turn-environments) |
| **Native tool parsing** | `ToolEnv`, `StatefulToolEnv` | [Environments: Tools](docs/environments.md#tool-environments) |
| **MCP tool integration** | `MCPEnv` | [Environments: MCP](docs/environments.md#mcp-tool-environments) |
| **Harness-in-sandbox** | `SandboxEnv`, `PythonEnv` | [Environments](docs/environments.md) |
| **Harness outside sandbox** | Any env with remote sandboxes | [Environments](docs/environments.md) |
| **No sandbox** | `SingleTurnEnv`, `MultiTurnEnv` | [Environments](docs/environments.md) |
| **Groupwise rewards** | Native support via `prime-rl` | [Training](docs/training.md) |
| **Weighted reward functions** | `Rubric` with `weight` param | [Environments: Rubrics](docs/environments.md#multiple-reward-functions) |
| **Intermediate reward tracking** | `Rubric.add_metric()` | [Environments: Metrics](docs/environments.md#metrics-and-monitor-rubrics) |
| **Multiple environments** | `RubricGroup`, multi-env evals | [Environments: RubricGroup](docs/environments.md#rubric-groups) |
| **Custom metrics/error handling** | Monitor rubrics, `add_metric()` | [Environments: Metrics](docs/environments.md#metrics-and-monitor-rubrics) |
| **Offline evals** | `prime eval run`, `prime eval tui` | [Evaluation](docs/evaluation.md) |
| **Resource management** | Sandbox lifecycle, `DatasetBuilder` | [Environments](docs/environments.md) |
| **Stateful interactions** | `StatefulToolEnv`, `setup_state` | [Environments: Stateful](docs/environments.md#stateful-tool-environments) |
| **Prompt optimization (GEPA)** | `prime gepa run` | [Training](docs/training.md) |


## Citation

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4 changes: 4 additions & 0 deletions verifiers/envs/integrations/textarena_env.py
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Expand Up @@ -136,6 +136,10 @@ def ta_to_hf(self) -> tuple[Dataset, Dataset | None]:
eval_dataset_rows = []
_, user_prompt = self.ta_env.get_observation()
words = self.ta_env.word_list
# Handle dict-based word lists (e.g. TwentyQuestions-v0 uses
# categorized words like {"animals": [...], "fruits": [...]})
if isinstance(words, dict):
words = [w for category in words.values() for w in category]
# set seed
random.seed(self.seed)
for i in range(self.num_train_examples + self.num_eval_examples):
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