ttt-discover-circle_packing_32-qwen3-8b

TTT-Discover training trace: Qwen/Qwen3-8B on 'You are an expert mathematician specializing in circle packing problems and comp'

Dataset Info

• Rows: 16
• Columns: 13

Columns

Column	Type	Description
run_id	Value('string')	Unique run identifier
model	Value('string')	Full model name
question	Value('string')	Prompt template (contains STATE_CTX placeholder for PUCT injection)
answer	Value('string')	Target value string
epoch	Value('int64')	No description provided
group_size	Value('int64')	Rollouts per group
avg_reward	Value('float64')	Mean reward across all rollouts
best_reward	Value('float64')	Max reward across all rollouts
loss	Value('float64')	Policy gradient training loss
reward_delta	Value('float64')	Change in avg_reward from previous step
rollouts	Value('string')	No description provided
config	Value('string')	JSON hyperparameters
timestamp	Value('string')	ISO timestamp

Generation Parameters

{
  "script_name": "scripts/run_ttt_discover.py",
  "model": "Qwen/Qwen3-8B",
  "description": "TTT-Discover training trace: Qwen/Qwen3-8B on 'You are an expert mathematician specializing in circle packing problems and comp'",
  "hyperparameters": {
    "task_id": "circle_packing_32",
    "num_steps": 50,
    "group_size": 64,
    "num_groups": 8,
    "total_rollouts": 512,
    "lr": 4e-05,
    "lora_rank": 32,
    "lora_alpha": 64,
    "temperature": 1.0,
    "max_tokens": 15000,
    "seed": 42,
    "start_step": 13,
    "resume_from": "/mnt/home/zsprague/code/JobToolKit/discover_output/circle_packing_32/lora_step_12"
  },
  "input_datasets": []
}

Usage

from datasets import load_dataset

dataset = load_dataset("reasoning-degeneration-dev/ttt-discover-circle_packing_32-qwen3-8b", split="train")
print(f"Loaded {len(dataset)} rows")

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This dataset is tracked in reasoning-degeneration-dev/PROJECT-MANIFEST