diff --git a/crates/ml/examples/train_mamba2.rs b/crates/ml/examples/train_mamba2.rs
deleted file mode 100644
index 8e66c1340..000000000
--- a/crates/ml/examples/train_mamba2.rs
+++ /dev/null
@@ -1,257 +0,0 @@
-//! MAMBA-2 Training Example
-//!
-//! Trains a MAMBA-2 state space model on real market data from DBN files.
-//! Uses continuous price sequences with microstructure features for next-timestep prediction.
-//!
-//! # Usage
-//!
-//! ```bash
-//! # Train with default parameters (100 epochs, real DBN data)
-//! cargo run -p ml --example train_mamba2 --release --features cuda
-//!
-//! # Custom parameters with specific DBN directory
-//! cargo run -p ml --example train_mamba2 --release --features cuda -- \
-//!   --epochs 500 \
-//!   --d-model 256 \
-//!   --n-layers 6 \
-//!   --seq-len 60 \
-//!   --dbn-dir test_data/real/databento/ml_training_small
-//!
-//! # Quick test with fewer epochs
-//! cargo run -p ml --example train_mamba2 --release --features cuda -- \
-//!   --epochs 10 \
-//!   --batch-size 4
-//! ```
-//!
-//! # Data Requirements
-//!
-//! - DBN files with OHLCV data (1-minute bars recommended)
-//! - At least 60-128 consecutive timesteps per symbol
-//! - Multiple files per symbol for better training data
-
-use anyhow::{Context, Result};
-use clap::Parser;
-use std::path::PathBuf;
-use tracing::info;
-use tracing_subscriber::FmtSubscriber;
-
-use ml::data_loaders::DbnSequenceLoader;
-use ml::trainers::mamba2::{Mamba2Hyperparameters, Mamba2Trainer};
-
-#[derive(Debug, Parser)]
-#[command(name = "train_mamba2", about = "Train MAMBA-2 model on market data")]
-struct Opts {
-    /// Number of training epochs
-    #[arg(long, default_value = "100")]
-    epochs: usize,
-
-    /// Learning rate
-    #[arg(long, default_value = "0.0001")]
-    learning_rate: f64,
-
-    /// Batch size (1-16 for 4GB VRAM)
-    #[arg(long, default_value = "8")]
-    batch_size: usize,
-
-    /// Model dimension (256, 512, 1024)
-    #[arg(long, default_value = "256")]
-    d_model: usize,
-
-    /// Number of layers (4-12)
-    #[arg(long, default_value = "6")]
-    n_layers: usize,
-
-    /// Sequence length
-    #[arg(long, default_value = "128")]
-    seq_len: usize,
-
-    /// Output directory for trained model
-    #[arg(long, default_value = "ml/trained_models")]
-    output_dir: String,
-
-    /// DBN data directory (contains .dbn files)
-    #[arg(long, default_value = "test_data/real/databento/ml_training_small")]
-    dbn_dir: String,
-
-    /// Train/validation split ratio
-    #[arg(long, default_value = "0.9")]
-    train_split: f64,
-
-    /// Verbose logging
-    #[arg(short, long)]
-    verbose: bool,
-}
-
-#[tokio::main]
-async fn main() -> Result<()> {
-    // Parse CLI options
-    let opts = Opts::parse();
-
-    // Setup logging
-    let level = if opts.verbose {
-        tracing::Level::DEBUG
-    } else {
-        tracing::Level::INFO
-    };
-
-    let subscriber = FmtSubscriber::builder().with_max_level(level).finish();
-    tracing::subscriber::set_global_default(subscriber)
-        .context("Failed to set tracing subscriber")?;
-
-    info!("🚀 Starting MAMBA-2 Training");
-    info!("Configuration:");
-    info!("  • Epochs: {}", opts.epochs);
-    info!("  • Learning rate: {}", opts.learning_rate);
-    info!("  • Batch size: {}", opts.batch_size);
-    info!("  • Model dimension: {}", opts.d_model);
-    info!("  • Number of layers: {}", opts.n_layers);
-    info!("  • Sequence length: {}", opts.seq_len);
-    info!("  • Output directory: {}", opts.output_dir);
-
-    // Create output directory
-    let output_path = PathBuf::from(&opts.output_dir);
-    if !output_path.exists() {
-        std::fs::create_dir_all(&output_path).context("Failed to create output directory")?;
-        info!("✅ Created output directory: {}", opts.output_dir);
-    }
-
-    // Configure MAMBA-2 hyperparameters
-    let hyperparams = Mamba2Hyperparameters {
-        learning_rate: opts.learning_rate,
-        batch_size: opts.batch_size,
-        d_model: opts.d_model,
-        n_layers: opts.n_layers,
-        state_size: 32,
-        dropout: 0.1,
-        epochs: opts.epochs,
-        seq_len: opts.seq_len,
-        grad_clip: 1.0,
-        weight_decay: 1e-4,
-        warmup_steps: 1000,
-    };
-
-    // Validate hyperparameters for VRAM constraint
-    hyperparams
-        .validate()
-        .context("Invalid hyperparameters for 4GB VRAM")?;
-
-    info!(
-        "✅ Hyperparameters validated (estimated VRAM: {}MB)",
-        hyperparams.estimate_memory_usage()
-    );
-
-    // Create MAMBA-2 trainer
-    let checkpoint_path = format!("{}/mamba2", opts.output_dir);
-    let mut trainer = Mamba2Trainer::new(hyperparams.clone(), Some(checkpoint_path))
-        .context("Failed to create MAMBA-2 trainer")?;
-
-    info!(
-        "✅ MAMBA-2 trainer initialized (job_id: {})",
-        trainer.job_id
-    );
-
-    // Load real DBN market data sequences
-    info!("\n📊 Loading DBN market data sequences...");
-    info!("  • DBN directory: {}", opts.dbn_dir);
-    info!("  • Sequence length: {}", opts.seq_len);
-    info!("  • Feature dimension: {}", opts.d_model);
-    info!(
-        "  • Train/val split: {:.1}/{:.1}",
-        opts.train_split * 100.0,
-        (1.0 - opts.train_split) * 100.0
-    );
-
-    let mut loader = DbnSequenceLoader::new(opts.seq_len, opts.d_model)
-        .await
-        .context("Failed to create DBN sequence loader")?;
-
-    let (train_data, val_data) = loader
-        .load_sequences(&opts.dbn_dir, opts.train_split)
-        .await
-        .context("Failed to load DBN sequences")?;
-
-    info!(
-        "✅ Loaded {} training sequences, {} validation sequences",
-        train_data.len(),
-        val_data.len()
-    );
-
-    if train_data.is_empty() {
-        return Err(anyhow::anyhow!(
-            "No training sequences loaded! Check DBN directory: {}",
-            opts.dbn_dir
-        ));
-    }
-
-    // Log sequence shape information
-    if let Some((input, target)) = train_data.first() {
-        info!("  • Input shape: {:?}", input.dims());
-        info!("  • Target shape: {:?}", target.dims());
-    }
-
-    // Set progress callback
-    let progress_callback =
-        std::sync::Arc::new(move |progress: ml::trainers::mamba2::TrainingProgress| {
-            if progress.epoch % 10 == 0 {
-                info!(
-                    "📊 Epoch {}/{} ({:.1}%): loss={:.6}, perplexity={:.2}",
-                    progress.epoch,
-                    progress.total_epochs,
-                    progress.progress_percentage,
-                    progress.metrics.loss,
-                    progress.metrics.perplexity
-                );
-            }
-        });
-
-    trainer.set_progress_callback(progress_callback);
-
-    // Train the model
-    info!("\n🏋️  Starting training...\n");
-    let start_time = std::time::Instant::now();
-
-    let training_history = trainer
-        .train(&train_data, &val_data)
-        .await
-        .context("Training failed")?;
-
-    let training_duration = start_time.elapsed();
-
-    // Print final metrics
-    info!("\n✅ Training completed successfully!");
-    info!("\n📊 Final Metrics:");
-    if let Some(final_epoch) = training_history.last() {
-        let loss_str = final_epoch
-            .loss
-            .map(|l| format!("{:.6}", l))
-            .unwrap_or_else(|| "N/A".to_string());
-        info!("  • Final loss: {}", loss_str);
-        let perplexity = final_epoch.loss.unwrap_or(f64::NAN).exp();
-        info!("  • Perplexity: {:.2}", perplexity);
-    }
-    info!("  • Best validation loss: {:.6}", trainer.best_val_loss);
-    info!("  • Epochs trained: {}", training_history.len());
-    info!(
-        "  • Training time: {:.1}s ({:.1} min)",
-        training_duration.as_secs_f64(),
-        training_duration.as_secs_f64() / 60.0
-    );
-
-    // Get training statistics
-    let stats = trainer.get_training_statistics();
-    info!("\n📈 Training Statistics:");
-    if let Some(&memory_mb) = stats.get("estimated_memory_mb") {
-        info!("  • Memory usage: {:.1}MB", memory_mb);
-    }
-    if let Some(&throughput) = stats.get("throughput_pps") {
-        info!("  • Throughput: {:.0} predictions/sec", throughput);
-    }
-
-    info!(
-        "\n💾 Model checkpoints saved to: {}",
-        trainer.checkpoint_path
-    );
-    info!("\n🎉 MAMBA-2 training complete!");
-
-    Ok(())
-}
diff --git a/crates/ml/examples/train_ppo.rs b/crates/ml/examples/train_ppo.rs
deleted file mode 100644
index be7e8da35..000000000
--- a/crates/ml/examples/train_ppo.rs
+++ /dev/null
@@ -1,406 +0,0 @@
-//! PPO Training Example with Real DataBento Market Data
-//!
-//! Trains a PPO model on real market data from DBN files with:
-//! - Real OHLCV data + technical indicators
-//! - Actual PnL-based rewards
-//! - GAE advantages on real price trajectories
-//! - Policy convergence validation (KL divergence > 0)
-//!
-//! # Usage
-//!
-//! ```bash
-//! # Train with default parameters (20 epochs)
-//! cargo run -p ml --example train_ppo --release --features cuda
-//!
-//! # Custom epochs and output path
-//! cargo run -p ml --example train_ppo --release --features cuda -- \
-//!   --epochs 50 \
-//!   --output-dir ml/trained_models \
-//!   --data-dir test_data/real/databento
-//! ```
-
-// Use mimalloc allocator for 10-25% performance improvement
-#[cfg(feature = "mimalloc-allocator")]
-use mimalloc::MiMalloc;
-#[cfg(feature = "mimalloc-allocator")]
-#[global_allocator]
-static GLOBAL: MiMalloc = MiMalloc;
-
-use anyhow::{Context, Result};
-use clap::Parser;
-use std::path::PathBuf;
-use tracing::{info, warn};
-use tracing_subscriber::FmtSubscriber;
-
-use ml::data_loaders::BarSamplingMethod;
-use ml::features::extraction::{extract_ml_features, OHLCVBar};
-use ml::real_data_loader::RealDataLoader;
-use ml::trainers::ppo::{PpoHyperparameters, PpoTrainer, PpoTrainingMetrics};
-
-#[derive(Debug, Parser)]
-#[command(name = "train_ppo", about = "Train PPO model on real market data")]
-struct Opts {
-    /// Number of training epochs (default: 20 for policy convergence)
-    #[arg(long, default_value = "20")]
-    epochs: usize,
-
-    /// Learning rate
-    #[arg(long, default_value = "0.0003")]
-    learning_rate: f64,
-
-    /// Batch size (512 recommended for value network stability, prevents -23.56 explained variance failure)
-    #[arg(long, default_value = "512")]
-    batch_size: usize,
-
-    /// Output directory for trained model
-    #[arg(long, default_value = "ml/trained_models")]
-    output_dir: String,
-
-    /// Data directory containing DBN files
-    #[arg(long, default_value = "test_data/real/databento")]
-    data_dir: String,
-
-    /// Symbol to train on (ZN.FUT has ~29K bars)
-    #[arg(long, default_value = "ZN.FUT")]
-    symbol: String,
-
-    /// Verbose logging
-    #[arg(short, long)]
-    verbose: bool,
-
-    /// Enable early stopping (recommended, use --no-early-stopping to disable)
-    #[arg(long)]
-    early_stopping: bool,
-
-    /// Disable early stopping
-    #[arg(long)]
-    no_early_stopping: bool,
-
-    /// Minimum value loss improvement percentage for plateau detection
-    #[arg(long, default_value = "2.0")]
-    min_value_loss_improvement: f64,
-
-    /// Minimum explained variance threshold
-    #[arg(long, default_value = "0.4")]
-    min_explained_variance: f64,
-
-    /// Plateau detection window size (epochs)
-    #[arg(long, default_value = "30")]
-    plateau_window: usize,
-
-    /// Alternative bar sampling method (time, tick, volume, dollar, imbalance, run)
-    #[arg(long, default_value = "time")]
-    bar_method: String,
-
-    /// Bar sampling threshold (tick count, volume, dollar value, imbalance, or run length)
-    #[arg(long)]
-    bar_threshold: Option<f64>,
-}
-
-#[tokio::main]
-async fn main() -> Result<()> {
-    // Parse CLI options
-    let opts = Opts::parse();
-
-    // Setup logging
-    let level = if opts.verbose {
-        tracing::Level::DEBUG
-    } else {
-        tracing::Level::INFO
-    };
-
-    let subscriber = FmtSubscriber::builder().with_max_level(level).finish();
-    tracing::subscriber::set_global_default(subscriber)
-        .context("Failed to set tracing subscriber")?;
-
-    #[cfg(feature = "mimalloc-allocator")]
-    info!("🚀 Using mimalloc allocator for improved performance");
-    #[cfg(not(feature = "mimalloc-allocator"))]
-    info!("ℹ️  Using system allocator (consider --features mimalloc-allocator for 10-25% speedup)");
-    info!("🚀 Starting PPO Training with Real DataBento Data");
-    info!("Configuration:");
-    info!("  • Epochs: {}", opts.epochs);
-    info!("  • Learning rate: {}", opts.learning_rate);
-    info!("  • Batch size: {}", opts.batch_size);
-    info!("  • GPU: CUDA MANDATORY (no CPU fallback)");
-    info!("  • Output directory: {}", opts.output_dir);
-    info!("  • Data directory: {}", opts.data_dir);
-    info!("  • Symbol: {}", opts.symbol);
-    info!("  • Bar sampling method: {}", opts.bar_method);
-    if let Some(threshold) = opts.bar_threshold {
-        info!("  • Bar threshold: {}", threshold);
-    }
-
-    // Determine early stopping (enabled by default, unless --no-early-stopping is specified)
-    let early_stopping_enabled = !opts.no_early_stopping;
-    info!(
-        "  • Early stopping: {}",
-        if early_stopping_enabled {
-            "enabled"
-        } else {
-            "disabled"
-        }
-    );
-    if early_stopping_enabled {
-        info!(
-            "    - Min value loss improvement: {}%",
-            opts.min_value_loss_improvement
-        );
-        info!(
-            "    - Min explained variance: {}",
-            opts.min_explained_variance
-        );
-        info!("    - Plateau window: {} epochs", opts.plateau_window);
-    }
-
-    // Create output directory
-    let output_path = PathBuf::from(&opts.output_dir);
-    if !output_path.exists() {
-        std::fs::create_dir_all(&output_path).context("Failed to create output directory")?;
-        info!("✅ Created output directory: {}", opts.output_dir);
-    }
-
-    // Configure alternative bar sampling (Wave B)
-    let bar_sampling = match opts.bar_method.as_str() {
-        "tick" => BarSamplingMethod::TickBars(opts.bar_threshold.unwrap_or(100.0) as usize),
-        "volume" => BarSamplingMethod::VolumeBars(opts.bar_threshold.unwrap_or(10000.0)),
-        "dollar" => BarSamplingMethod::DollarBars(opts.bar_threshold.unwrap_or(2_000_000.0)),
-        "imbalance" => BarSamplingMethod::ImbalanceBars(opts.bar_threshold.unwrap_or(1000.0)),
-        "run" => BarSamplingMethod::RunBars(opts.bar_threshold.unwrap_or(50.0) as usize),
-        _ => BarSamplingMethod::TimeBars,
-    };
-
-    info!("✅ Bar sampling configured: {:?}", bar_sampling);
-
-    // Load real market data from DBN files
-    info!("\n📊 Loading real market data from DBN files...");
-    let mut loader = RealDataLoader::new(&opts.data_dir);
-
-    // Note: RealDataLoader will need to accept bar_sampling parameter
-    // This requires updating RealDataLoader to use alternative bar sampling
-    let bars = loader
-        .load_symbol_data(&opts.symbol)
-        .await
-        .context(format!("Failed to load data for symbol: {}", opts.symbol))?;
-
-    info!("✅ Loaded {} OHLCV bars for {}", bars.len(), opts.symbol);
-
-    // Extract features and indicators
-    info!("\n🔧 Extracting features and technical indicators...");
-    let features = loader
-        .extract_features(&bars)
-        .context("Failed to extract features")?;
-    let _indicators = loader
-        .calculate_indicators(&bars)
-        .context("Failed to calculate indicators")?;
-
-    info!("✅ Feature extraction complete:");
-    info!("  • OHLCV bars: {}", features.prices.len());
-    info!("  • Returns: {}", features.returns.len());
-    info!("  • Volume: {}", features.volume.len());
-    info!("  • Indicators: 10 technical indicators");
-
-    // Build PPO state vectors using 54-feature extraction pipeline
-    // Features 0-4: OHLCV (normalized)
-    // Features 5-14: Technical indicators (10)
-    // Remaining features from extraction pipeline
-    info!("\n🏗️  Extracting 54-dimensional feature vectors...");
-
-    // Convert RealDataLoader bars to OHLCVBar format for feature extraction
-    let ohlcv_bars: Vec<OHLCVBar> = bars
-        .iter()
-        .map(|bar| OHLCVBar {
-            timestamp: bar.timestamp,
-            open: bar.open,
-            high: bar.high,
-            low: bar.low,
-            close: bar.close,
-            volume: bar.volume,
-        })
-        .collect();
-
-    // Extract 54-dimensional feature vectors (requires 50-bar warmup)
-    let feature_vectors =
-        extract_ml_features(&ohlcv_bars).context("Failed to extract 54-dimensional features")?;
-
-    info!(
-        "✅ Extracted {} feature vectors (dim=54, warmup bars skipped=50)",
-        feature_vectors.len()
-    );
-
-    // Convert FeatureVector ([f64; 54]) to Vec<Vec<f32>> for PPO trainer
-    let state_dim = 54;
-    let market_data: Vec<Vec<f32>> = feature_vectors
-        .iter()
-        .map(|fv| fv.iter().map(|&v| v as f32).collect())
-        .collect();
-
-    // Validate state dimensions
-    if let Some(first_state) = market_data.first() {
-        if first_state.len() != state_dim {
-            return Err(anyhow::anyhow!(
-                "State dimension mismatch: expected {}, got {}",
-                state_dim,
-                first_state.len()
-            ));
-        }
-    }
-
-    // Configure PPO hyperparameters
-    let hyperparams = PpoHyperparameters {
-        learning_rate: opts.learning_rate,
-        batch_size: opts.batch_size,
-        gamma: 0.99,
-        clip_epsilon: 0.2,
-        vf_coef: 0.5,
-        ent_coef: 0.01,
-        gae_lambda: 0.95,
-        rollout_steps: 2048,
-        minibatch_size: opts.batch_size,
-        epochs: opts.epochs,
-        early_stopping_enabled,
-        min_value_loss_improvement_pct: opts.min_value_loss_improvement,
-        min_explained_variance: opts.min_explained_variance,
-        plateau_window: opts.plateau_window,
-        min_epochs_before_stopping: 50,
-    };
-
-    // Create PPO trainer with real data state dimension
-    let trainer = PpoTrainer::new(
-        hyperparams.clone(),
-        state_dim,
-        &opts.output_dir,
-        true, // CUDA always required
-        None, // Single environment (standard mode)
-    )
-    .context("Failed to create PPO trainer")?;
-
-    info!("✅ PPO trainer initialized (state_dim={})", state_dim);
-
-    // Create progress callback with convergence tracking
-    let mut policy_updates = 0;
-    let mut kl_divergence_history = Vec::new();
-
-    let progress_callback = |metrics: PpoTrainingMetrics| {
-        // Track policy updates (KL divergence > 0 indicates policy changed)
-        if metrics.kl_divergence > 0.0 {
-            policy_updates += 1;
-        }
-        kl_divergence_history.push(metrics.kl_divergence);
-
-        info!(
-            "📊 Epoch {}/{}: policy_loss={:.4}, value_loss={:.4}, kl_div={:.6}, expl_var={:.4}, mean_reward={:.4}",
-            metrics.epoch,
-            hyperparams.epochs,
-            metrics.policy_loss,
-            metrics.value_loss,
-            metrics.kl_divergence,
-            metrics.explained_variance,
-            metrics.mean_reward
-        );
-    };
-
-    // Train the model
-    info!("\n🏋️  Starting training...\n");
-    let start_time = std::time::Instant::now();
-
-    let final_metrics = trainer
-        .train(market_data, progress_callback)
-        .await
-        .context("Training failed")?;
-
-    let training_duration = start_time.elapsed();
-
-    // Print final metrics
-    info!("\n✅ Training completed successfully!");
-    info!("\n📊 Final Metrics:");
-    info!("  • Policy loss: {:.6}", final_metrics.policy_loss);
-    info!("  • Value loss: {:.6}", final_metrics.value_loss);
-    info!("  • KL divergence: {:.6}", final_metrics.kl_divergence);
-    info!(
-        "  • Explained variance: {:.4}",
-        final_metrics.explained_variance
-    );
-    info!("  • Mean reward: {:.4}", final_metrics.mean_reward);
-    info!("  • Std reward: {:.4}", final_metrics.std_reward);
-    info!("  • Entropy: {:.4}", final_metrics.entropy);
-    info!(
-        "  • Training time: {:.1}s ({:.1} min)",
-        training_duration.as_secs_f64(),
-        training_duration.as_secs_f64() / 60.0
-    );
-
-    // Validate policy convergence
-    info!("\n🔍 Policy Convergence Analysis:");
-    info!("  • Total epochs: {}", hyperparams.epochs);
-    info!("  • Policy updates (KL > 0): {}", policy_updates);
-    info!(
-        "  • Policy update rate: {:.1}%",
-        (policy_updates as f64 / hyperparams.epochs as f64) * 100.0
-    );
-
-    // Calculate KL divergence statistics
-    let kl_mean = kl_divergence_history.iter().sum::<f32>() / kl_divergence_history.len() as f32;
-    let kl_max = kl_divergence_history
-        .iter()
-        .copied()
-        .fold(f32::NEG_INFINITY, f32::max);
-    let kl_min = kl_divergence_history
-        .iter()
-        .copied()
-        .fold(f32::INFINITY, f32::min);
-
-    info!("  • KL divergence (mean): {:.6}", kl_mean);
-    info!("  • KL divergence (max): {:.6}", kl_max);
-    info!("  • KL divergence (min): {:.6}", kl_min);
-
-    // Convergence validation
-    if final_metrics.kl_divergence > 0.0 {
-        info!("  ✅ PASS: Policy updates detected (KL divergence > 0)");
-    } else {
-        warn!("  ⚠️  WARN: No policy updates in final epoch (KL divergence = 0)");
-        warn!("          This may indicate learning rate too low or convergence");
-    }
-
-    // Value function validation
-    if final_metrics.explained_variance > 0.5 {
-        info!("  ✅ PASS: Value network learning (explained variance > 0.5)");
-    } else {
-        warn!("  ⚠️  WARN: Value network may need tuning (explained variance < 0.5)");
-    }
-
-    // Checkpoint is already saved by trainer (every 10 epochs)
-    let final_checkpoint = output_path.join(format!(
-        "ppo_checkpoint_epoch_{}.safetensors",
-        hyperparams.epochs
-    ));
-
-    info!(
-        "\n💾 Final checkpoint saved to: {}",
-        final_checkpoint.display()
-    );
-    info!("\n🎉 PPO training complete with real DataBento data!");
-    info!("📁 Model files saved to: {}", opts.output_dir);
-
-    info!("\n📈 Training Summary:");
-    info!("  • Data source: Real DataBento OHLCV ({})", opts.symbol);
-    info!("  • Training samples: {}", bars.len());
-    info!("  • State dimension: {}", state_dim);
-    info!("  • Features: OHLCV + 10 technical indicators + log returns");
-    info!(
-        "  • Policy updates: {}/{} epochs ({:.1}%)",
-        policy_updates,
-        hyperparams.epochs,
-        (policy_updates as f64 / hyperparams.epochs as f64) * 100.0
-    );
-    info!(
-        "  • Convergence: {}",
-        if final_metrics.kl_divergence > 0.0 {
-            "✅ Achieved"
-        } else {
-            "⚠️  Check logs"
-        }
-    );
-
-    Ok(())
-}
diff --git a/scripts/entrypoint-generic.sh b/scripts/entrypoint-generic.sh
index d6f71c344..9fecb221d 100755
--- a/scripts/entrypoint-generic.sh
+++ b/scripts/entrypoint-generic.sh
@@ -52,17 +52,17 @@ if [ $# -eq 0 ]; then
     log "  Override Docker CMD with your training command:"
     log ""
     log "EXAMPLES:"
-    log "  # DQN Training (100 epochs)"
-    log "  CMD: /runpod-volume/binaries/train_dqn --parquet-file /runpod-volume/test_data/ES_FUT_small.parquet --epochs 100 --output-dir /runpod-volume/models"
+    log "  # RL Training (DQN on ES futures, walk-forward)"
+    log "  CMD: /runpod-volume/binaries/train_baseline_rl --model dqn --symbol ES.FUT --data-dir /runpod-volume/data --epochs 100"
     log ""
-    log "  # TFT Training (50 epochs, GPU enabled)"
-    log "  CMD: /runpod-volume/binaries/train_tft_parquet --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --epochs 50 --use-gpu --output-dir /runpod-volume/models"
+    log "  # RL Training (PPO on NQ futures)"
+    log "  CMD: /runpod-volume/binaries/train_baseline_rl --model ppo --symbol NQ.FUT --data-dir /runpod-volume/data --epochs 200"
     log ""
-    log "  # PPO Training (200 epochs)"
-    log "  CMD: /runpod-volume/binaries/train_ppo --parquet-file /runpod-volume/test_data/NQ_FUT_180d.parquet --epochs 200 --output-dir /runpod-volume/models"
+    log "  # Supervised Training (TFT on parquet data)"
+    log "  CMD: /runpod-volume/binaries/train_baseline_supervised --model tft --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --epochs 50"
     log ""
-    log "  # MAMBA-2 Training (30 epochs)"
-    log "  CMD: /runpod-volume/binaries/train_mamba2_parquet --parquet-file /runpod-volume/test_data/6E_FUT_180d.parquet --epochs 30 --output-dir /runpod-volume/models"
+    log "  # Supervised Training (Mamba2 on parquet data)"
+    log "  CMD: /runpod-volume/binaries/train_baseline_supervised --model mamba2 --parquet-file /runpod-volume/test_data/6E_FUT_180d.parquet --epochs 30"
     log ""
     log "Container is ready and waiting for commands..."
     log "To deploy with a custom command, override the Docker CMD in your deployment script"