FlashOptim can improve the training efficiency of Behavior Learning models by reducing memory requirements for storing optimization states.

FlashOptim, when applied to Behavior Learning (imitation learning / offline RL) model training, reduces optimizer state memory consumption by ≥30% compared to standard Adam/AdamW optimizers, while maintaining or improving final policy performance (within ±2% of baseline reward/accuracy) and reducing wall-clock training time by ≥10% on GPU hardware with ≥16GB VRAM, across model sizes ranging from 10M to 500M parameters.

Minimum Viable Test (MVT): Train a 100M-parameter behavior cloning transformer on the D4RL HalfCheetah-medium-v2 dataset using (a) AdamW baseline and (b) FlashOptim, on a single A100-40GB GPU. Measure peak GPU memory, training throughput (samples/sec), and normalized score after 100K gradient steps. This constitutes a go/no-go gate before full validation.

Full Validation: 3×3×2 factorial design — 3 model sizes (10M, 100M, 500M params) × 3 tasks (locomotion, manipulation, navigation) × 2 optimizers (AdamW, FlashOptim) — with 3 random seeds each = 54 total training runs. Add ablations for batch size (256, 1024, 4096) and precision (FP32, BF16).

# FlashOptim Behavior Learning Validation — Implementation Sketch
# ================================================================

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import d4rl, gym
# from flashoptim import FlashOptim  # hypothetical import

# --- 1. Model Definition (Decision Transformer, scalable) ---
class BehaviorTransformer(nn.Module):
    def __init__(self, state_dim, action_dim, n_layers, d_model, n_heads):
        super().__init__()
        self.embed = nn.Linear(state_dim + action_dim + 1, d_model)
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=n_heads, 
            dim_feedforward=4*d_model, batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)
        self.action_head = nn.Linear(d_model, action_dim)
    
    def forward(self, states, actions, returns_to_go):
        # [B, T, state_dim+action_dim+1] -> [B, T, action_dim]
        x = torch.cat([states, actions, returns_to_go.unsqueeze(-1)], dim=-1)
        x = self.embed(x)
        x = self.transformer(x)
        return self.action_head(x)

# Model size configurations
MODEL_CONFIGS = {
    '10M':  {'n_layers': 2,  'd_model': 256,  'n_heads': 4},
    '100M': {'n_layers': 12, 'd_model': 768,  'n_heads': 12},
    '500M': {'n_layers': 24, 'd_model': 1536, 'n_heads': 16},
}

# --- 2. Optimizer Factory ---
def get_optimizer(model, optimizer_type='adamw', lr=1e-4):
    if optimizer_type == 'adamw':
        return torch.optim.AdamW(
            model.parameters(), lr=lr, 
            weight_decay=0.1, betas=(0.9, 0.999)
        )
    elif optimizer_type == 'flashoptim':
        # FlashOptim: quantized optimizer states (8-bit or block-wise)
        # Key parameters: quantization_bits=8, block_size=2048
        return FlashOptim(
            model.parameters(), lr=lr,
            weight_decay=0.1, betas=(0.9, 0.999),
            quantization_bits=8,  # core memory reduction mechanism
            block_size=2048
        )
    else:
        raise ValueError(f"Unknown optimizer: {optimizer_type}")

# --- 3. Memory Profiling Wrapper ---
class MemoryProfiler:
    def __init__(self):
        self.measurements = []
    
    def measure(self, label):
        torch.cuda.synchronize()
        mem_mb = torch.cuda.max_memory_allocated() / 1024**2
        self.measurements.append({'label': label, 'peak_mb': mem_mb})
        torch.cuda.reset_peak_memory_stats()
        return mem_mb
    
    def optimizer_state_size(self, optimizer):
        """Directly measure optimizer state tensor sizes in MB"""
        total_bytes = 0
        for group in optimizer.param_groups:
            for p in group['params']:
                state = optimizer.state[p]
                for k, v in state.items():
                    if isinstance(v, torch.Tensor):
                        total_bytes += v.element_size() * v.nelement()
        return total_bytes / 1024**2

# --- 4. Training Loop ---
def train_epoch(model, optimizer, dataloader, device, profiler, step_count):
    model.train()
    total_loss = 0.0
    criterion = nn.MSELoss()
    
    for batch_idx, (states, actions, rtg) in enumerate(dataloader):
        states, actions, rtg = states.to(device), actions.to(device), rtg.to(device)
        
        optimizer.zero_grad()
        pred_actions = model(states, actions[:, :-1], rtg)
        loss = criterion(pred_actions, actions)
        loss.backward()
        
        # Gradient clipping (standard for transformers)
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        
        total_loss += loss.item()
        step_count += 1
        
        # Profile every 1000 steps
        if step_count % 1000 == 0:
            peak_mem = profiler.measure(f'step_{step_count}')
            opt_state_mem = profiler.optimizer_state_size(optimizer)
            print(f"Step {step_count}: Loss={loss.item():.4f}, "
                  f"Peak VRAM={peak_mem:.1f}MB, "
                  f"Opt States={opt_state_mem:.1f}MB")
    
    return total_loss / len(dataloader), step_count

# --- 5. Evaluation ---
def evaluate_policy(model, env_name, n_episodes=100, device='cuda'):
    env = gym.make(env_name)
    model.eval()
    scores = []
    
    for ep in range(n_episodes):
        state = env.reset()
        ep_return = 0.0
        done = False
        # Simplified rollout (full implementation uses context window)
        while not done:
            with torch.no_grad():
                state_t = torch.FloatTensor(state).unsqueeze(0).unsqueeze(0).to(device)
                # ... (full DT inference with return conditioning)
                action = model.action_head(...)  # simplified
            state, reward, done, _ = env.step(action.cpu().numpy())
            ep_return += reward
        scores.append(ep_return)
    
    # D4RL normalized score
    normalized = d4rl.get_normalized_score(env_name, scores) * 100
    return {'mean': float(torch.tensor(scores).mean()), 
            'std': float(torch.tensor(scores).std()),
            'normalized': normalized}

# --- 6. Main Experiment Runner ---
def run_experiment(model_size, task, optimizer_type, seed, n_steps=100000):
    torch.manual_seed(seed)
    device = torch.device('cuda')
    
    # Load dataset
    env = gym.make(task)
    dataset = env.get_dataset()  # D4RL offline dataset
    # ... (dataset preprocessing into DataLoader)
    
    # Initialize model
    cfg = MODEL_CONFIGS[model_size]
    model = BehaviorTransformer(
        state_dim=env.observation_space.shape[0],
        action_dim=env.action_space.shape[0],
        **cfg
    ).to(device)
    
    optimizer = get_optimizer(model, optimizer_type)
    profiler = MemoryProfiler()
    
    results = {
        'model_size': model_size, 'task': task,
        'optimizer': optimizer_type, 'seed': seed,
        'memory_profile': [], 'loss_curve': [],
        'throughput_sps': [], 'final_score': None
    }
    
    step_count = 0
    import time
    t0 = time.time()
    
    while step_count < n_steps:
        loss, step_count = train_epoch(
            model, optimizer, dataloader, device, profiler, step_count
        )
        results['loss_curve'].append(loss)
    
    elapsed = time.time() - t0
    results['throughput_sps'] = (n_steps * BATCH_SIZE) / elapsed
    results['final_score'] = evaluate_policy(model, task)
    results['memory_profile'] = profiler.measurements
    results['optimizer_state_mb'] = profiler.optimizer_state_size(optimizer)
    
    return results

# --- 7. Statistical Analysis ---
def compute_memory_reduction(adamw_results, flashoptim_results):
    """Compute % memory reduction with confidence interval"""
    import numpy as np
    from scipy import stats
    
    adamw_mem = [r['optimizer_state_mb'] for r in adamw_results]
    flash_mem = [r['optimizer_state_mb'] for r in flashoptim_results]
    
    reduction_pct = [(a - f) / a * 100 for a, f in zip(adamw_mem, flash_mem)]
    mean_reduction = np.mean(reduction_pct)
    ci = stats.t.interval(0.95, len(reduction_pct)-1, 
                          loc=mean_reduction, 
                          scale=stats.sem(reduction_pct))
    t_stat, p_val = stats.ttest_rel(adamw_mem, flash_mem)
    
    return {
        'mean_reduction_pct': mean_reduction,
        'ci_95': ci,
        'p_value': p_val,
        'significant': p_val < 0.05
    }

# --- 8. Experiment Grid ---
EXPERIMENT_GRID = [
    (model_size, task, optimizer, seed)
    for model_size in ['10M', '100M', '500M']
    for task in ['halfcheetah-medium-v2', 'hopper-medium-expert-v2', 'kitchen-mixed-v0']
    for optimizer in ['adamw', 'flashoptim']
    for seed in [42, 123, 456]
]

# Run all experiments (parallelized across GPUs in practice)
all_results = []
for model_size, task, optimizer, seed in EXPERIMENT_GRID:
    result = run_experiment(model_size, task, optimizer, seed)
    all_results.append(result)
    # Save checkpoint after each run
    torch.save(result, f'results/{model_size}_{task}_{optimizer}_{seed}.pt')