OpenThinkerAgent-8B-ColdStartSFTForRL API & Inference Endpoint

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

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.

Citation

text

@misc{openthoughts-agent,  author = {Team, OpenThoughts-Agent},  title = {{OpenThoughts-Agent: Data Recipes for Agentic Models}},  howpublished = {https://www.openthoughts.ai/blog/agent},  year = {2026}}

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.

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.

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

Citation

text

@misc{openthoughts-agent,  author = {Team, OpenThoughts-Agent},  title = {{OpenThoughts-Agent: Data Recipes for Agentic Models}},  howpublished = {https://www.openthoughts.ai/blog/agent},  year = {2026}}

@misc{openthoughts-agent,  author = {Team, OpenThoughts-Agent},  title = {{OpenThoughts-Agent: Data Recipes for Agentic Models}},  howpublished = {https://www.openthoughts.ai/blog/agent},  year = {2026}}

OpenThinkerAgent-8B-ColdStartSFTForRL

README