---
title: SylloGym — Deductive Reasoning Environment
sdk: docker
app_port: 7860
base_path: /web
colorFrom: blue
colorTo: green
tags:
- openenv
- reasoning
- rlvr
- legalbench
- grpo
- deductive-reasoning
- first-order-logic
---

SylloGym — Deductive Reasoning Environment

SylloGym is a multi-dataset deductive reasoning environment for training LLMs via reinforcement learning. The model receives a rule, a set of facts, and a question — and must apply strict logical deduction to reach the correct conclusion.

How to interact

1. Click Reset to get a new reasoning problem
2. Fill in the reasoning field with your chain-of-thought (wrap in <reasoning>...</reasoning>)
3. Fill in the answer field with your final answer (wrap in <answer>...</answer>)
4. Click Step to submit and see your reward

Example:

reasoning: <reasoning>The rule states that if a contract contains a confidentiality clause, it is an NDA. The facts show a confidentiality clause is present. Therefore, this is an NDA.</reasoning>
answer: <answer>Yes</answer>

Reward breakdown

| Component | Points | Condition |
|-----------|--------|-----------|
| Format | +0.1 | Both <reasoning> and <answer> tags present |
| Answer | +1.0 | Correct answer |
| Reasoning | +0.2 | Non-trivial reasoning (>50 chars, not just restating the answer) |
| Max | 1.3| |

Datasets & Tasks

SylloGym covers 6 datasets and 20 tasks across different reasoning domains:

| Dataset | Tasks | Type | Difficulty |
|---------|-------|------|------------|
| LegalBench | diversity_1–6, ucc_v_common_law, abercrombie, hearsay, telemarketing_sales_rule | Yes/No or multi-class | 1–5 |
| Knights & Knaves | knights_knaves | Who is knight/knave? | 1–3 |
| ProofWriter | proofwriter_d1–d5 | True/False | 1–5 |
| FOLIO | folio | True/False/Uncertain | 2–5 |
| RuleBreakers | rulebreakers_mt, rulebreakers_ds | True/False | 2 |
| FOL-NLI | fol_nli | entailment/contradiction/neutral | 2–5 |

Reset options

Pass optional parameters to reset() to control task selection:

• task_mode="mixed" (default) — weighted random across all datasets


• task_mode="single" + task_name="hearsay" — restrict to one task


• seed=42 — reproducible sampling

Connect from code

from syllogym_env import SylloGymEnv, SylloAction

env = SylloGymEnv(base_url="https://huggingface.co/spaces/farffadet/syllogym-env")
env.connect()

result = env.reset(task_mode="mixed")
obs = result.observation
print(obs.rule)
print(obs.facts)
print(obs.question)

result = env.step(SylloAction(
    reasoning="<reasoning>Applying the rule to the facts...</reasoning>",
    answer=f"<answer>{obs.valid_answers[0]}</answer>",
))
print(f"Reward: {result.reward}")
env.disconnect()

About

Competition: [OpenEnv Challenge](https://huggingface.co/openenv) by Meta PyTorch × HuggingFace × Unsloth
Training: GRPO (Group Relative Policy Optimization) via TRL + Unsloth
Base model: Qwen2.5-3B-Instruct