AStar-Thought-Latent-Qwen3.5-0.8B-deepseek-v3.2-speciale-openr1-math-3k-filtered-v10-angle90-Qwen3.6-27B-v2.12-strc0.1
This model is a fine-tuned version of Qwen/Qwen3.6-27B for the A*-Thought-V2 framework, as described in the paper A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM.
A*-Thought-V2 is an explicit-implicit interleaved efficient reasoning architecture guided by LLM dynamics. By interweaving implicit latent-space reasoning with explicit text, it performs lossless compression of the chain-of-thought: aligned reasoning steps remain explicit text, while deviating steps are compressed into continuous latent tokens.
Code for data compression, training, and evaluation is available at the AStar-Thought GitHub repository.
Training and evaluation data
Trained using the A*-Thought-V2 compression pipeline on the OpenR1-Math-3k dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3.0
Training results
- Final training loss: 0.3388
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2