{"schema_version":"onlylabs.public_signal.v1","title":"OpenBMB (MiniCPM) Model: openbmb/RLPR-Llama3.1-8B-Inst","description":"OpenBMB (MiniCPM) model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/7facf54c-19a5-4b66-be9d-85c843727df5","json_url":"https://onlylabs.fyi/signals/7facf54c-19a5-4b66-be9d-85c843727df5/signal.json","generated_at":"2026-06-11T02:50:41.266851+00:00","org":{"slug":"openbmb","name":"OpenBMB (MiniCPM)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/openbmb","dossier_json_url":"https://onlylabs.fyi/labs/openbmb/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/7facf54c-19a5-4b66-be9d-85c843727df5","signal_json":"https://onlylabs.fyi/signals/7facf54c-19a5-4b66-be9d-85c843727df5/signal.json","source":"https://huggingface.co/openbmb/RLPR-Llama3.1-8B-Inst","lab_dossier":"https://onlylabs.fyi/labs/openbmb","lab_dossier_json":"https://onlylabs.fyi/labs/openbmb/dossier.json","analysis":"https://onlylabs.fyi/analysis/openbmb","analysis_json":"https://onlylabs.fyi/analysis/openbmb/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openbmb/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":"OpenBMB (MiniCPM) published openbmb/RLPR-Llama3.1-8B-Inst. 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This eliminates the need for external verifiers and specialized fine-tuning, offering broad applicability and effectively handling complex, diverse answers. * 🛠️ **Innovative Reward & Training Framework:** * Features a robust **Probability-based Reward (PR)** using average decoding probabilities of reference answers for higher quality, debiased reward signals, outperforming naive sequence likelihood. * Implements an **standard deviation filtering** mechanism that dynamically..."},"evidence_pages":[{"url":"https://huggingface.co/openbmb/RLPR-Llama3.1-8B-Inst/raw/main/README.md","final_url":"https://huggingface.co/openbmb/RLPR-Llama3.1-8B-Inst/raw/main/README.md","title":"openbmb/RLPR-Llama3.1-8B-Inst model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:50:41.266851+00:00","bytes":3024,"raw_path":"1f024c6f78fc8aeb47f0082eb4f5cbfbd1e7124cb3f01532236740225e3b3bc3.md","content_hash":"33c27feff7f6e7cd614e93545ae97f50032e0676044cdaa1d01fa02f010a58cb","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: apache-2.0 datasets: - openbmb/RLPR-train language: - en tags: - text-generation-inference library_name: transformers pipeline_tag: text-generation --- Model Card for RLPR-Llama3.1-8B-Inst [GitHub](https://github.com/openbmb/RLPR) | [Paper](https://arxiv.org/abs/2506.18254) **RLPR-Llama3.1-8B-Inst** is trained from Llama3.1-8B-Inst with the [RLPR](https://github.com/openbmb/RLPR) framework, which eliminates reliance on external verifiers and is simple and generalizable for more domains. Model Details Key Features * 💡 **Verifier-Free Reasoning Enhancement:** RLPR pioneers reinforcement learning for reasoning tasks by leveraging the LLM's intrinsic generation probability as a direct reward signal. 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