reasoning-degeneration-dev/t1-strategy-arena-frozenlake-backward_chaining-together_ai-moonshotai-kimi-k2-instruct-e271b21c
t1-strategy-arena-frozenlake-backward_chaining-together_ai-moonshotai-kimi-k2-instruct-e271b21c Strategy compliance baseline — FrozenLake arena evaluation. No strategy instruction was given. This is the baseline to observe the model's natural reasoning patterns across game turns. Per-turn reasoning is extracted from transcripts for manual review. Results Metric Value Win rate 10/10 (100.0%) Avg turns 5.6 Errors 0 Details… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/t1-strategy-arena-frozenlake-backward_chaining-together_ai-moonshotai-kimi-k2-instruct-e271b21c.
t1-strategy-arena-frozenlake-backwardchaining-togetherai-moonshotai-kimi-k2-instruct-e271b21c
Strategy compliance baseline — FrozenLake arena evaluation.
No strategy instruction was given. This is the baseline to observe the model's natural reasoning patterns across game turns. Per-turn reasoning is extracted from transcripts for manual review.
Results
Details
Columns
transcript: Full game transcript (JSON array of turn objects)per_turn_reasoning: Extracted reasoning per turn (JSON array of strings)reward: Final game reward (1.0 = win, negative = loss)num_turns: Number of turns played
Usage
from datasets import load_dataset
import json
ds = load_dataset("reasoning-degeneration-dev/t1-strategy-arena-frozenlake-backward_chaining-together_ai-moonshotai-kimi-k2-instruct-e271b21c", split="train")
for row in ds:
reasoning = json.loads(row["per_turn_reasoning"])
print(f"Game {row['game_id']}: {len(reasoning)} turns, reward={row['reward']}")
for t, r in enumerate(reasoning):
if r.strip():
print(f" Turn {t}: {r[:200]}...")Tracked in [reasoning-degeneration-dev/PROJECT-MANIFEST](https://huggingface.co/datasets/reasoning-degeneration-dev/PROJECT-MANIFEST)