OpenResearcher vs Search-R1
TIGER-AI-Lab's fully open deep-research recipe: 96K long-horizon trajectories (adopted by NVIDIA Nemotron), a 30B-A3B model hitting 54.8% BrowseComp-Plus, training code and eval harness. — versus — RL framework for training reasoning-and-search interleaved LLMs — the open recipe behind DeepSeek-R1-style search agents: PPO/GRPO on veRL, any search backend, models and data on HF. Two papers.
Two open deep-research training recipes: OpenResearcher ships trajectories + a trained model; Search-R1 ships the RL framework to train your own from scratch.
| OpenResearcher | Search-R1 | |
|---|---|---|
| Stars | 1.1k | 5.3k |
| Forks | 104 | 477 |
| Language | Python | Python |
| License | — | Apache-2.0 |
| Last activity | 2 months ago | 8 months ago |
| Topics | training | training |
| Curated connections | 3 | 2 |
OpenResearcher — the curator's take
The open counterpoint to closed Deep Research products — and the data is the crown jewel: 100+-turn research trajectories distilled from GPT-OSS-120B over a self-built 11B-token retriever corpus (no search-API bills at generation scale), good enough that NVIDIA folded it into Nemotron 3 Ultra. The 30B-A3B model beats GPT-4.1, Claude-Opus-4 and Gemini-2.5-Pro on BrowseComp-Plus. Reproducing anything is a real commitment: the setup assumes 8×A100, training lives in a Megatron-LM fork, and the local retriever needs Java + tevatron. ⚠ No LICENSE file in the repo — clarify terms before commercial use. Pick DeepDive for the KG-synthesis + multi-turn-RL recipe; OpenResearcher for large-scale SFT distillation with everything — data, model, eval — actually released.
Search-R1 — the curator's take
The reference open recipe for teaching a model WHEN to search, not just how to prompt it — full experiment logs on W&B, models and data released, and Tinker featured it. If you're building deep-research agents by training rather than orchestration, start here. When NOT: this is research infrastructure — GPUs, veRL, retrieval corpora — not something you bolt onto an app; for prompt-time search agents an orchestration framework is the cheaper 95%.