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README.md

@@ -1,117 +1,51 @@
 ---
 license: mit
 tags:
-- degen_test_1_countdown
-- budget-32k
 - dfs_baseline
-- dfs_baseline
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: question
-    dtype: string
-  - name: metadata
-    struct:
-    - name: expression
-      dtype: string
-    - name: numbers
-      list: int64
-    - name: operators_used
-      list: string
-    - name: target
-      dtype: int64
-  - name: task_source
-    dtype: string
-  - name: responses_by_sample
-    list:
-      list: string
-  - name: tokens_by_sample
-    list: int64
-  - name: num_rounds_by_sample
-    list: int64
-  - name: strategy_type
-    dtype: string
-  - name: model
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6598221
-    num_examples: 100
-  download_size: 347108
-  dataset_size: 6598221
+- countdown
+- baseline
 ---
 
 # countdown-dfs-baseline-32k
 
-Countdown reasoning with dfs_baseline strategy to 32768 tokens
+DFS baseline: systematic enumeration of operator combinations
 
 ## Dataset Info
 
 - **Rows**: 100
-- **Columns**: 17
+- **Columns**: 8
 
 ## Columns
 
 | Column | Type | Description |
 |--------|------|-------------|
-| question | Value('string') | The countdown problem statement |
-| metadata | Value('string') | JSON with target number, available numbers, and solution |
-| task_source | Value('string') | Task type identifier (countdown) |
-| formatted_prompt | List({'content': Value('string'), 'role': Value('string')}) | Initial prompt messages sent to the model |
-| responses_by_sample | List(List(Value('string'))) | Full model response [sample_idx][round_idx] = response string |
-| tokens_by_sample | List(Value('int64')) | Total token count for each sample (based on continuation_text) |
-| num_rounds_by_sample | List(Value('int64')) | Number of continuation rounds for each sample |
-| continuation_prompts_by_sample | List(List(Value('null'))) | Continuation prompts used [sample_idx][round_idx] = prompt string |
-| continuation_text_by_sample | List(List(Value('null'))) | Truncated text used for continuation [sample_idx][round_idx]. For budget_forcing: truncated at first </think> tag. For other strategies: same as responses_by_sample. |
-| strategy_type | Value('string') | Prompting strategy: 'iterative', 'budget_forcing', or 'random_noise' |
-| termination_reason_by_sample | List(Value('string')) | Why each sample terminated: 'max_rounds_reached', 'budget_reached', or 'answer_correct' |
-| correct_round_by_sample | List(Value('int64')) | Round number where correct answer was first found (None if never correct) |
-| smart_termination_enabled | Value('bool') | Whether smart termination was enabled for this run |
-| retry_count_by_sample | List(Value('int64')) | Total retry attempts per sample due to empty or too-short responses (0 if no retries needed) |
-| answers_by_sample | List(List(List(Value('string')))) | Extracted answer spans [sample][round][answer_idx] |
-| evals_by_sample | List(List(List(Value('bool')))) | Correctness evaluations [sample][round][answer_idx] |
-| eval_metadata_by_sample | List(List(List({'answer_block': Value('string'), 'error': Value('string'), 'final_answer': Value('float64'), 'is_correct': Value('bool')}))) | Evaluation details [sample][round][answer_idx] |
+| question | Value('string') | *No description provided* |
+| metadata | {'expression': Value('string'), 'numbers': List(Value('int64')), 'operators_used': List(Value('string')), 'target': Value('int64')} | *No description provided* |
+| task_source | Value('string') | *No description provided* |
+| responses_by_sample | List(List(Value('string'))) | *No description provided* |
+| tokens_by_sample | List(Value('int64')) | *No description provided* |
+| num_rounds_by_sample | List(Value('int64')) | *No description provided* |
+| strategy_type | Value('string') | *No description provided* |
+| model | Value('string') | *No description provided* |
 
 
 ## Generation Parameters
 
 ```json
 {
-  "script_name": "stage_01_generate_reasoning.py",
+  "script_name": "stage_01_dfs_baseline.py",
   "model": "dfs_baseline",
+  "description": "DFS baseline: systematic enumeration of operator combinations",
   "hyperparameters": {
-    "temperature": 0.7,
-    "max_tokens_per_round": 32768,
-    "target_total_tokens": 32768,
-    "samples": 1,
-    "strategy": "dfs_baseline",
-    "max_rounds": null,
-    "min_tokens": null,
-    "max_retries_per_round": -1
+    "token_budget": 32000,
+    "num_examples": 100,
+    "arg_count": 4,
+    "samples": 1
   },
-  "input_datasets": [],
-  "description": "Countdown reasoning with dfs_baseline strategy to 32768 tokens",
-  "experiment": "degen_test_1_countdown",
-  "strategy": "dfs_baseline",
-  "arg_count": 4,
-  "seed": 42,
-  "smart_termination": false,
-  "max_rounds": null,
-  "min_tokens": null,
-  "inference_backend": "curator",
-  "status": "completed",
-  "completed_at": "2026-01-03T20:33:58.477853"
+  "input_datasets": []
 }
 ```
 
-## Experiment Documentation
-
-For complete experiment details, see [https://github.com/TAUR-Lab/Reasoning-Horizons/tree/main/experiments/degen_test_1_countdown](https://github.com/TAUR-Lab/Reasoning-Horizons/tree/main/experiments/degen_test_1_countdown)
-
 ## Usage
 
 ```python