Project | Code | Collection
# OpenThinkerAgent-8B-ColdStartSFTForRL **OpenThoughts-Agent** is an open-source effort to curate the best datasets for training agents. Our release includes [datasets](https://huggingface.co/collections/open-thoughts/openthinker-agent), [models](https://huggingface.co/collections/open-thoughts/openthinker-agent) and our [research codebase](https://github.com/open-thoughts/OpenThoughts-Agent). [OpenThinkerAgent-8B-ColdStartSFTForRL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL) is the **cold-start, pre-RL base** of the OpenThoughts-Agent 8B SFT→RL recipe. It is post-trained from [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) with full-parameter SFT on the cold-start [OpenThoughts-Agent-SFT-ColdStartForRL-10K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K) dataset. Its purpose is to give the model the agentic interaction format and tool-use behaviour needed to make subsequent reinforcement learning stable; it is then RL-trained to produce [OpenThinkerAgent-8B-RL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-RL). > **Architecture note.** Although the upstream artifact carries a `GLM-4.7` label (which refers to the *teacher* that generated the SFT trajectories, not the student), this model is a **Qwen3-8B**. Its `config.json` reports `model_type: qwen3`, `architectures: ["Qwen3ForCausalLM"]`, 36 layers, hidden size 4096, 32 attention heads / 8 KV heads, and a 40,960-token context — i.e. standard Qwen3-8B. - **Homepage:** https://www.openthoughts.ai/blog/agent - **Repository:** https://github.com/open-thoughts/OpenThoughts-Agent # Model details - **Base model:** [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) - **Architecture:** Qwen3 (`Qwen3ForCausalLM`), 36 layers, hidden size 4096, 32 attention heads, 8 KV heads, RoPE θ = 1e6 - **Context length:** 40,960 tokens (max position embeddings) - **Vocabulary:** 151,936 tokens - **Precision:** bf16 - **Role in pipeline:** cold-start SFT checkpoint (pre-RL base) # Position in the SFT → RL recipe 1. [OpenThoughts-Agent-SFT-ColdStartForRL-10K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K) — cold-start SFT trajectories. 2. **[OpenThinkerAgent-8B-ColdStartSFTForRL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL)** — this model (Qwen3-8B after cold-start SFT, the pre-RL base). 3. [OpenThoughts-Agent-RL-5K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-RL-5K) — on-policy RL tasks. 4. [OpenThinkerAgent-8B-RL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-RL) — the final RL'd checkpoint (step 45). # Training data Trained on [OpenThoughts-Agent-SFT-ColdStartForRL-10K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K) (**9,437** (task, trajectory) pairs): SWE-Smith sandboxed coding tasks with tests, solved by a teacher model in the **terminus-2** harness inside Daytona sandboxes, oracle-verified (120s verifier timeout). # Training procedure Full-parameter SFT (LLaMA-Factory). Hyperparameters as recorded by the trainer: - learning_rate: 4e-05 - lr_scheduler_type: cosine, warmup_ratio 0.1 - train_batch_size: 1 per device × 8 devices × gradient_accumulation_steps 2 → total_train_batch_size 16 - optimizer: AdamW (fused), betas (0.9, 0.98), eps 1e-08 - num_epochs: 7 - seed: 42 - precision: bf16 - final train loss: ≈ 0.303 (4,130 global steps) ### Framework versions - Transformers 4.57.6 - PyTorch 2.9.0+cu128 - Datasets 4.4.1 - Tokenizers 0.22.2 # Intended uses & limitations This checkpoint is intended as the **starting point for agentic RL**, not as a final deployable agent. It has learned the agentic format and tool-use conventions of the terminus-2 harness from a relatively small cold-start set; its standalone agentic performance is expected to be below the RL-trained successor [OpenThinkerAgent-8B-RL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-RL). As with the base Qwen3-8B, outputs may be incorrect or unsafe and should not be executed without review. No standalone agentic-benchmark numbers are published for this cold-start checkpoint. # Links - 🌐 [OpenThoughts-Agent project page](https://www.openthoughts.ai/blog/agent) - 💻 [OpenThoughts-Agent GitHub repository](https://github.com/open-thoughts/OpenThoughts-Agent) - 📚 [OpenThinker-Agent collection](https://huggingface.co/collections/open-thoughts/openthinker-agent) - 🧠 [Training dataset: OpenThoughts-Agent-SFT-ColdStartForRL-10K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K) - 🧠 [RL tasks: OpenThoughts-Agent-RL-5K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-RL-5K) - 🤖 [Final RL model: OpenThinkerAgent-8B-RL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-RL) # Citation ``` @misc{openthoughts-agent, author = {Team, OpenThoughts-Agent}, title = {{OpenThoughts-Agent: Data Recipes for Agentic Models}}, howpublished = {https://www.openthoughts.ai/blog/agent}, year = {2026} } ```