refactor(ml): consolidate 21 training binaries into 2 unified baselines

Replace 20 per-model training examples with:
- train_baseline_rl: DQN + PPO (renamed from train_baseline)
- train_baseline_supervised: TFT, Mamba2, Liquid, TGGN, TLOB, KAN, xLSTM, Diffusion
  via model factory + UnifiedTrainable generic training loop

Update Dockerfile.training (16→7 binaries), train.sh MODEL_BINARY map,
and job-template.yaml default. -12,759 lines.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

infra/scripts/train.sh

@@ -34,16 +34,16 @@ ALL_MODELS=(dqn ppo tft mamba2 tggn tlob liquid kan xlstm diffusion)
 
 # --- Model-to-binary mapping ----------------------------------------------
 declare -A MODEL_BINARY=(
-  [dqn]=train_dqn_es_fut
-  [ppo]=train_ppo_parquet
-  [tft]=train_tft_dbn
-  [mamba2]=train_mamba2_dbn
-  [tggn]=train_tggn_dbn
-  [tlob]=train_tlob
-  [liquid]=train_liquid_dbn
-  [kan]=train_kan_dbn
-  [xlstm]=train_xlstm_dbn
-  [diffusion]=train_diffusion_dbn
+  [dqn]=train_baseline_rl
+  [ppo]=train_baseline_rl
+  [tft]=train_baseline_supervised
+  [mamba2]=train_baseline_supervised
+  [tggn]=train_baseline_supervised
+  [tlob]=train_baseline_supervised
+  [liquid]=train_baseline_supervised
+  [kan]=train_baseline_supervised
+  [xlstm]=train_baseline_supervised
+  [diffusion]=train_baseline_supervised
 )
 
 EVAL_BINARY="evaluate_baseline"
@@ -129,6 +129,8 @@ build_args() {
   local binary="${MODEL_BINARY[$model]}"
   local args=("$binary")
 
+  # Both unified binaries accept --model to select the specific model
+  args+=("--model" "$model")
   args+=("--symbol" "$SYMBOL")
   args+=("--max-steps-per-epoch" "$MAX_STEPS")
   args+=("--data-dir" "$DATA_DIR")