OpenThinkerAgent-8B-ColdStartSFTForRL
OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our release includes datasets, models and our research codebase.
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 with full-parameter SFT on the cold-start 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.
Architecture note. Although the upstream artifact carries a
GLM-4.7label (which refers to the teacher that generated the SFT trajectories, not the student), this model is a Qwen3-8B. Itsconfig.jsonreportsmodel_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
- 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
- OpenThoughts-Agent-SFT-ColdStartForRL-10K — cold-start SFT trajectories.
- OpenThinkerAgent-8B-ColdStartSFTForRL — this model (Qwen3-8B after cold-start SFT, the pre-RL base).
- OpenThoughts-Agent-RL-5K — on-policy RL tasks.
- OpenThinkerAgent-8B-RL — the final RL'd checkpoint (step 45).
Training data
Trained on 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. 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
- 💻 OpenThoughts-Agent GitHub repository
- 📚 OpenThinker-Agent collection
- 🧠 Training dataset: OpenThoughts-Agent-SFT-ColdStartForRL-10K
- 🧠 RL tasks: OpenThoughts-Agent-RL-5K
- 🤖 Final RL model: 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}
}