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zurichquants/OpenThinkerAgent-8B-ColdStartSFTForRL

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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-4.7 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
—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 — 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 — on-policy RL tasks.
4.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
—lrschedulertype: cosine, warmup_ratio 0.1
—trainbatchsize: 1 per device × 8 devices × gradientaccumulationsteps 2 → totaltrainbatch_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.

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}
}