Llama 3.2 architecture
The captured configuration identifies LlamaForCausalLM with model type llama and Transformers support.
Open Source Model Profile · Menlo
ReZero-v0.1-llama-3.2-3b-it-grpo-250404
ReZero-v0.1-llama-3.2-3b-it-grpo-250404 is a 3.21B-parameter Llama text-generation model from Menlo. According to the model card, it trains Llama-3.2-3B for persistent search behavior with GRPO.
- Publisher
- Menlo
- Task
- text-generation
- Model type
- llama
- License
- llama3.2
- Library
- transformers
- Publication status
- Accepted · not indexed
Model overview
ReZero-v0.1-llama-3.2-3b-it-grpo-250404 is published by Menlo as a text-generation model. The captured configuration identifies LlamaForCausalLM, and Safetensors metadata reports about 3.21B parameters. According to the model card, it is a GRPO-trained Llama-3.2-3B model focused on search behavior.
Recorded capabilities
Search-behavior training
According to the model card, training emphasizes query refinement and persistent search with synthetic engines rather than static memorization.
Llama-3.2-3B backbone
According to the model card and hub tags, the backbone is Llama-3.2-3B, with the GRPO checkpoint reported as the 3B ReZero-v0.1 model.
Documented demo setup
According to the model card, the publisher documents a Gradio app.py demo, repository setup, and a Tavily API key for the web-search demo.
Published experiment log
According to the model card, the log records Apollo Mission Report runs, reward-weight detail, and step, hardware, and accuracy notes.
Use cases in the source record
- Search-behavior experiments using the documented Gradio demo and retry-oriented querying.
- Conversational text-generation tests consistent with the captured pipeline tag and conversational hub tagging.
Limitations and unknowns
- No context-window value was extracted from this record.
- The only accuracy figure is the publisher-reported 31.25% best at step 400 for one experiment; no broader evaluation table was extracted.
- Provider state is historical snapshot data and should be refreshed before being presented as current.
Source and provenance
Source: Menlo/ReZero-v0.1-llama-3.2-3b-it-grpo-250404
Captured: Unknown. Processed: 2026-09-07T19:35:13.171873+00:00.
ReZero: Enhancing LLM search ability by trying one-more-time ReZero trains a small language model to develop effective search behaviors instead of memorizing static data. It interacts with multiple synthetic search engines, each with unique retrieval mechanisms, to refine queries and persist in searching until it finds exact answers. The project focuses on reinforcement learning, preventing overfitting, and optimizing for efficiency in real-world search applications. Quick Demo 🚀 Run the interactive web interface to see ReZero in action: python app.py This will launch a Gradio interface where you can interact with the model and tes…
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