Built from LiquidAI/LFM2-2.6B through a full training pipeline: Supervised Fine-Tuning on synthetic data, followed by two rounds of Reinforcement Learning (CISPO) in a verifiable Tic Tac Toe environment.

This model was developed as part of 🎓 LLM RL Environments Lil Course, a hands-on course on building RL environments for Language Models, where models learn from rewards, not examples. It walks through the full process of turning a small open model into a specialist that outperforms a large proprietary one on a specific task (Tic Tac Toe).

🤗🕹️ Play against Mr. Tic Tac Toe

Training pipeline

Step	Model	Method
1. SFT warm-up	anakin87/LFM2-2.6B-ttt-sft	SFT on 174 synthetic games from `gpt-5-mini`
2. RL round 1	anakin87/LFM2-2.6B-ttt-rl + merged	CISPO, 600 steps, opponents at 20-70% random
3. RL round 2	anakin87/LFM2-2.6B-ttt-rl-2 + this model	CISPO, 400 steps, opponents at 0-25% random, temp 1.25

Evaluation

100 games per setting. The model plays as X (first mover) against a Minimax-based opponent.

Model vs random opponent	% Wins	% Draws	% Losses	% Follows format	% Games w invalid moves
openai/gpt-5-mini	90	9	1	100	0
LiquidAI/LFM2-2.6B	40	11	49	27.8	40
anakin87/LFM2-2.6B-mr-tictactoe	90	10	0	100	0

Model vs optimal opponent	% Wins	% Draws	% Losses	% Follows format	% Games w invalid moves
openai/gpt-5-mini	0	76	24	100	0
LiquidAI/LFM2-2.6B	0	11	89	24.7	43
anakin87/LFM2-2.6B-mr-tictactoe	0	97	3	99.8	0

Training details

Algorithm: CISPO (two rounds), using Verifiers RLTrainer
Environment: anakin87/tictactoe (Verifiers environment)
LoRA rank: 8
Hardware: 2x NVIDIA RTX Pro 6000 (round 1), 2x NVIDIA H200 (round 2)
Training time: ~8 hours per round
W&B project: LFM2-2.6B Tic Tac Toe

anakin87/LFM2-2.6B-mr-tictactoe

LFM2-2.6B-mr-tictactoe

Training pipeline

Evaluation

Training details

anakin87/
LFM2-2.6B-mr-tictactoe