thealper2/lfm2-350m-2048-lora

LFM2-350M 2048 policy (LoRA adapter)

LiquidAI/LFM2-350M fine-tuned with supervised LoRA to map a 2048 board and its legal moves to the next move (LEFT/UP/RIGHT/DOWN), trained on the 2048 demonstrations of IntelligenceLab/Cos-Play-Cold-Start.

Model

Base model	LiquidAI/LFM2-350M (354M params; 16 layers: 10 short-conv + 6 GQA attention)
Fine-tuning	LoRA, r=16, alpha=32, dropout=0.05
LoRA targets	q_proj, k_proj, v_proj, out_proj, in_proj, w1, w2, w3 (92 modules)
Trainable params	5,996,544
Precision	bfloat16
Context used	~384 tokens max (prompts ≈ 85–90 tokens)

Input / output format

LFM2 chat template, single user turn, greedy decoding. The prompt lists only legal (board-changing) moves in the fixed order LEFT, UP, RIGHT, DOWN:

Game: 2048

State:
[0, 2, 0, 0]
[0, 4, 2, 0]
[0, 0, 8, 0]
[0, 0, 0, 0]

Available actions:
LEFT
UP
RIGHT
DOWN

Choose the next action:

Target completion: one action word followed by <|im_end|> (training loss is computed only on these tokens).

Training data

Source	IntelligenceLab/Cos-Play-Cold-Start, configs grpo_action_taking_twenty_forty_eight + episodes_twenty_forty_eight
Demonstrator	GPT-5.4 agent, 60 episodes, ≤200 steps each, max tile 256
Examples	8125 steps; 1151 no-op demonstrations removed
Board source	episode state (action-taking prompts only contain a text summary); 289 boards repaired for a uint8 display overflow (256 shown as 0)
Split	episode-level 80%/10%/10%, seed 42
Episodes (train/val/test)	48/6/6
Examples (train/val/test)	5668/731/575
Action distribution	LEFT 50.1%, UP 20.7%, DOWN 18.2%, RIGHT 11.0%

Training procedure

Hyperparameter	Value
Epochs	3
Learning rate / schedule	0.0001 / cosine, warmup ratio 0.05
Batch size	1 × grad. accumulation 16 = 16
Weight decay / max grad norm	0.0 / 1.0
Optimizer	AdamW (torch fused)
Checkpoint selection	best validation action_accuracy (greedy first-token action accuracy)
Optimizer steps	1065
Best checkpoint	checkpoint-750 (val accuracy 60.5%)
Hardware / runtime	NVIDIA GeForce RTX 5060 Ti, 34.7 min, peak 2.66 GB
Seed	42

Evaluation

Offline action prediction (held-out test episodes)

Metric	Value
Examples	575
Accuracy	61.6%
Macro accuracy	44.2%
Accuracy LEFT (n=292)	89.0%
Accuracy UP (n=114)	40.4%
Accuracy RIGHT (n=63)	4.8%
Accuracy DOWN (n=106)	42.5%
Invalid generation rate	0.0%
Illegal action rate	0.0%
Reference: majority / random / heuristic agreement	60.2% / 29.2% / 43.0%

Autonomous gameplay

Local 2048 environment (4×4, spawn 2/4 with p=0.9/0.1, reward = merged tile values; validated against the dataset: 100.0% transition match). 100 games, seeds 0–99, played until no legal move remains. Invalid outputs get one retry with a stricter prompt, then a deterministic fallback (first legal move).

Agent	Mean score	Median	Mean max tile	≥512	≥1024	2048 rate	Mean steps	Invalid	Retry	Fallback
this model	2,677.1	2,600	220.8	7	0	0.0%	228.9	0.0%	0.0%	0.0%
greedy heuristic	7,753.6	7,242	572.2	69	24	2.0%	518.2	0.0%	0.0%	0.0%
majority action	2,545.6	2,478	206.1	5	0	0.0%	224.4	0.0%	0.0%	0.0%
random legal	1,018.9	872	96.5	0	0	0.0%	112.9	0.0%	0.0%	0.0%

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-350M", dtype=torch.bfloat16).cuda()
model = PeftModel.from_pretrained(model, "thealper2/lfm2-350m-2048-lora").merge_and_unload()
tok = AutoTokenizer.from_pretrained("thealper2/lfm2-350m-2048-lora")

board = [[0, 2, 0, 0], [0, 4, 2, 0], [0, 0, 8, 0], [0, 0, 0, 0]]
legal = ["LEFT", "UP", "RIGHT", "DOWN"]  # only moves that change the board
prompt = ("Game: 2048\n\nState:\n" + "\n".join(str(r) for r in board)
          + "\n\nAvailable actions:\n" + "\n".join(legal) + "\n\nChoose the next action:")
text = tok.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True)
ids = tok(text, return_tensors="pt", add_special_tokens=False).to(model.device)
out = model.generate(**ids, max_new_tokens=6, do_sample=False)
print(tok.decode(out[0, ids["input_ids"].shape[1]:], skip_special_tokens=True))  # e.g. "LEFT"

Board rows must be formatted exactly as [a, b, c, d] and actions listed in the order LEFT, UP, RIGHT, DOWN.

Limitations

• Imitation of a weak demonstrator: training episodes stop at ≤200 steps and never exceed tile 256, so late-game boards with 512+ tiles are outside the training distribution.
• Board-only policy: no move history, no lookahead; one move per forward pass.
• Offline accuracy measures agreement with the demonstrator, not move quality; several moves are often equally good in 2048.
• Trained and evaluated on 4×4 boards with the prompt format above only.