{"schema_version":"onlylabs.public_signal.v1","title":"StepFun Model: stepfun-ai/Qwen2.5-32B-DialogueReason","description":"StepFun model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/c9398311-d900-468c-a9b2-38535881d505","json_url":"https://onlylabs.fyi/signals/c9398311-d900-468c-a9b2-38535881d505/signal.json","generated_at":"2026-06-11T02:50:42.837216+00:00","org":{"slug":"stepfun","name":"StepFun","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/stepfun","dossier_json_url":"https://onlylabs.fyi/labs/stepfun/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/c9398311-d900-468c-a9b2-38535881d505","signal_json":"https://onlylabs.fyi/signals/c9398311-d900-468c-a9b2-38535881d505/signal.json","source":"https://huggingface.co/stepfun-ai/Qwen2.5-32B-DialogueReason","lab_dossier":"https://onlylabs.fyi/labs/stepfun","lab_dossier_json":"https://onlylabs.fyi/labs/stepfun/dossier.json","analysis":"https://onlylabs.fyi/analysis/stepfun","analysis_json":"https://onlylabs.fyi/analysis/stepfun/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/stepfun/evidence.json","category":"https://onlylabs.fyi/neolabs","category_json":"https://onlylabs.fyi/neolabs.json","category_feed":"https://onlylabs.fyi/neolabs/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neolab","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"StepFun published stepfun-ai/Qwen2.5-32B-DialogueReason. 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We train the model using [Open-Reasoner-Zero](https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero) data through rule-based reinforcement learning. 🧠 Key Features - Qwen2.5-32B-Base as the foundation. - Use Rule-Based RL to achieve dialogue reasoning. - With dynamic agent initialization to adapt to various scenarios. - With flexible environment configuration to set up task-specific contexts. - With multi-turn dialogue reasoning to incrementally solve problems. Example System: > The User asks a question, and the Assistant writes a masterpiece play depicting experts (picked based on the topic with concrete names) solving the question in a ultra-detailed dialogue. The response is formatted as: <play>the play goes here</play>\\\\n<answer> if asked to write code, then code here surrounded by ```. Otherwise, answer here with \\\\boxed{answer} emphasized</answer>. User: > Give me a detailed explanation of PPO in RL Assistant: > ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61775e8c1e3e9ecbf77ddee8/hluQG4Yz75FE5HFbxkKnh.png)"},"evidence_pages":[{"url":"https://huggingface.co/stepfun-ai/Qwen2.5-32B-DialogueReason/raw/main/README.md","final_url":"https://huggingface.co/stepfun-ai/Qwen2.5-32B-DialogueReason/raw/main/README.md","title":"stepfun-ai/Qwen2.5-32B-DialogueReason model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:50:42.837216+00:00","bytes":1219,"raw_path":"db5c08c7df43e25922913a2f18d55747667f981b067f96763c2177de5802ad4d.md","content_hash":"656ec12e8962616703d3c5938132399fdb9ca935acbece034ac9b49d550e9d29","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: apache-2.0 --- Introduction Qwen2.5-32B-DialogueReason is a dialogue-based reasoning model built on Qwen2.5-32B-Base. 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