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Model: google/gemini-3.1-pro-preview

Total steps: 7

Messages: 22

UserStep 0

Task: optimization-variance-reduction

Variance Reduction for Stochastic Optimization

Research Question

Design an improved variance reduction strategy for stochastic gradient descent on finite-sum optimization problems. Your method should accelerate convergence compared to vanilla mini-batch SGD by reducing the variance of gradient estimates.

Background

Many machine learning problems take the form of finite-sum optimization:

min_x  F(x) = (1/n) * sum_{i=1}^{n} f_i(x)

Standard SGD uses a stochastic gradient from a random mini-batch, which has variance proportional to 1/b (where b is the batch size). Variance reduction methods use auxiliary information (snapshots, recursive corrections, momentum) to reduce this variance, enabling faster convergence -- often achieving linear convergence rates for strongly convex problems where SGD only achieves sublinear rates.

Key methods in this area include SVRG (periodic full gradient + control variate), SARAH (recursive gradient correction), STORM (momentum-based online variance reduction), SPIDER, and PAGE.

Task

Modify the VarianceReductionOptimizer class in custom_vr.py (lines 286-370). You must implement:

__init__(self, model, lr, l2_reg, loss_type, n_train, batch_size, device): Initialize any state needed for variance reduction (snapshot parameters, running gradient estimates, buffers, etc.)
train_one_epoch(self, X_train, y_train): Train for one epoch over the data, returning a dict with at least 'avg_loss' (and optionally 'full_grad_count' if you use full gradient computations)

The default implementation is vanilla mini-batch SGD. Your goal is to design a variance reduction mechanism that improves convergence.

Interface

Available helper functions (FIXED, use these for gradient computation):

compute_full_gradient(model, X_train, y_train, loss_type, l2_reg, device)
# -> returns list of gradient tensors (one per parameter)

compute_stochastic_gradient(model, X_batch, y_batch, loss_type, l2_reg)
# -> returns list of gradient tensors for a mini-batch

compute_loss_on_batch(model, X_batch, y_batch, loss_type, l2_reg)
# -> returns scalar loss tensor

Constraints

You may call compute_full_gradient at most once per epoch
Parameter updates must use p.data.add_(...) or similar in-place operations
Must work across all three problems with the same code
The learning rate (self.lr) and L2 regularization (self.l2_reg) are fixed
Do not modify the model architecture, loss function, or evaluation code

Evaluation

Problems:
- logistic: L2-regularized multinomial logistic regression on MNIST (convex, n=60K, 20 epochs)
- mlp: 2-layer MLP on CIFAR-10 (non-convex, n=50K, 40 epochs)
- conditioned: L2-regularized linear regression on synthetic ill-conditioned data (strongly convex, kappa=100, n=10K, 30 epochs)
Metrics: best_test_accuracy (logistic, mlp; higher is better) and best_test_mse (conditioned; lowe

AssistantStep 1edit

Tool Input

1{
2  "filename": "opt-vr-bench/custom_vr.py",
3  "start_line": 311,
4  "content": "class VarianceReductionOptimizer:\n    def __init__(self, model: nn.Module, lr: float, l2_reg: float,\n                 loss_type: str, n_train: int, batch_size: int,\n                 device: torch.device):\n        self.model = model\n        self.lr = lr\n        self.l2_reg = l2_reg\n        self.loss_type = loss_type\n        self.n_train = n_train\n        self.batch_size = batch_size\n        self.device = device\n        self.params = list(model.parameters())\n        self.m = [torch.zeros_like(p) for p in self.params]\n        self.momentum = 0.9\n\n    def train_one_epoch(self, X_train: torch.Tensor,\n                        y_train: torch.Tensor) -> dict:\n        self.model.train()\n        n = X_train.size(0)\n\n        # Compute full gradient at snapshot\n        snapshot_params = [p.data.clone() for p in self.params]\n        full_grad = compute_full_gradient(\n            self.model, X_train, y_train, self.loss_type,\n            self.l2_reg, self.device\n        )\n\n        indices = torch.randperm(n)\n        total_loss = 0.0\n        n_batches = 0\n\n        for start in range(0, n, self.batch_size):\n            end = min(start + self.batch_size, n)\n            idx = indices[start:end]\n            Xb = X_train[idx].to(self.device)\n            yb = y_train[idx].to(self.device)\n\n            # grad at current params\n            self.model.zero_grad()\n            loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            loss.backward()\n            curr_grad = [p.grad.clone() for p in self.params]\n\n            # replace params with snapshot to get grad at snapshot\n            curr_params = [p.data.clone() for p in self.params]\n            for p, sp in zip(self.params, snapshot_params):\n                p.data.copy_(sp)\n\n            self.model.zero_grad()\n            with torch.no_grad():\n                # For grad computation only. But we need to backward.\n                pass\n            loss_snap = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            loss_snap.backward()\n            snap_grad = [p.grad.clone() for p in self.params]\n\n            # restore current params\n            for p, cp in zip(self.params, curr_params):\n                p.data.copy_(cp)\n\n            # Update with SVRG estimator + momentum\n            with torch.no_grad():\n                for i, p in enumerate(self.params):\n                    # v_t = curr_grad - snap_grad + full_grad\n                    v = curr_grad[i] - snap_grad[i] + full_grad[i]\n                    self.m[i].mul_(self.momentum).add_(v, alpha=1.0 - self.momentum)\n                    p.data.add_(self.m[i], alpha=-self.lr)\n\n            total_loss += loss.item()\n            n_batches += 1\n\n        return {\"avg_loss\": total_loss / max(n_batches, 1), \"full_grad_count\": 1}\n",
5  "op": "replace",
6  "end_line": 370,
7  "after_line": 285
8}

Tool ResultStep 1

OK: Replaced lines 311..370 with 72 line(s) in opt-vr-bench/custom_vr.py. Editable range: 286–382.

[Current file: opt-vr-bench/custom_vr.py | editable: 286–382 | total: 501 lines]
   283| 
   284| 
   285| # ============================================================================
   286  # EDITABLE -- Variance Reduction Strategy (lines 286-370)
   287  # ============================================================================
   288  # Design a variance reduction mechanism for stochastic gradient computation.
   289  # You may modify ONLY this section.
   290  #
   291  # Interface contract:
   292  #   - VarianceReductionOptimizer.__init__(model, lr, l2_reg, loss_type, n_train, batch_size, device)
   293  #   - VarianceReductionOptimizer.train_one_epoch(X_train, y_train)
   294  #     -> trains for one epoch, returns dict with 'avg_loss'
   295  #
   296  # Available helper functions (FIXED, defined above):
   297  #   - compute_full_gradient(model, X_train, y_train, loss_typ

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)


## Seed 42

### logistic (scripts/logistic.sh)
Training progress (last 5 epochs):
TRAIN_METRICS: epoch=16 avg_loss=0.240150 time=1.06s grad_comps=15008
TRAIN_METRICS: epoch=17 avg_loss=0.239571 time=1.08s grad_comps=15946
TRAIN_METRICS: epoch=18 avg_loss=0.239032 time=1.07s grad_comps=16884
TRAIN_METRICS: epoch=19 avg_loss=0.238466 time=1.06s grad_comps=17822
TRAIN_METRICS: epoch=20 avg_loss=0.237925 time=1.14s grad_comps=18760
Evaluation progress (last 5 evals):
EVAL_METRICS: epoch=16 test_loss=0.265884 test_accuracy=92.540000 best_test_accuracy=92.570000
EVAL_METRICS: epoch=17 test_loss=0.265964 test_accuracy=92.550000 best_test_accuracy=92.570000
EVAL_METRICS: epoch=18 test_loss=0.266051 test_accuracy=92.550000 best_test_accuracy=92.570000
EVAL_METRICS: epoch=19 test_loss=0.266181 test_accuracy=92.570000 best_test_accuracy=92.570000
EVAL_METRICS: epoch=20 test_loss=0.266284 test_accuracy=92.570000 best_test_accu

AssistantStep 3edit

Tool Input

1{
2  "filename": "opt-vr-bench/custom_vr.py",
3  "end_line": 382,
4  "op": "replace",
5  "start_line": 311,
6  "after_line": 285,
7  "content": "class VarianceReductionOptimizer:\n    def __init__(self, model: nn.Module, lr: float, l2_reg: float,\n                 loss_type: str, n_train: int, batch_size: int,\n                 device: torch.device):\n        self.model = model\n        self.lr = lr\n        self.l2_reg = l2_reg\n        self.loss_type = loss_type\n        self.n_train = n_train\n        self.batch_size = batch_size\n        self.device = device\n        self.params = list(model.parameters())\n        \n        n_steps_per_epoch = max(1, n_train // batch_size)\n        self.momentum = 1.0 - 1.0 / math.sqrt(n_steps_per_epoch)\n        self.d = None\n        self.prev_params = None\n\n    def train_one_epoch(self, X_train: torch.Tensor,\n                        y_train: torch.Tensor) -> dict:\n        self.model.train()\n        n = X_train.size(0)\n        a = self.momentum\n        \n        # Periodic reset of STORM estimator with exact full gradient\n        self.d = compute_full_gradient(\n            self.model, X_train, y_train, self.loss_type,\n            self.l2_reg, self.device\n        )\n        self.prev_params = [p.data.clone() for p in self.params]\n\n        indices = torch.randperm(n)\n        total_loss = 0.0\n        n_batches = 0\n\n        for start in range(0, n, self.batch_size):\n            end = min(start + self.batch_size, n)\n            idx = indices[start:end]\n            Xb = X_train[idx].to(self.device)\n            yb = y_train[idx].to(self.device)\n\n            # Grad at current params\n            self.model.zero_grad()\n            loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            loss.backward()\n            curr_grad = [p.grad.clone() for p in self.params]\n\n            # Grad at previous params (same batch)\n            curr_params = [p.data.clone() for p in self.params]\n            for p, pp in zip(self.params, self.prev_params):\n                p.data.copy_(pp)\n\n            self.model.zero_grad()\n            with torch.no_grad(): pass \n            loss_prev = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            loss_prev.backward()\n            prev_grad = [p.grad.clone() for p in self.params]\n\n            # Restore current params\n            for p, cp in zip(self.params, curr_params):\n                p.data.copy_(cp)\n\n            # STORM update\n            with torch.no_grad():\n                for i, p in enumerate(self.params):\n                    # d_t = g_t + a * (d_{t-1} - g'_{t-1})\n                    self.d[i] = curr_grad[i] + a * (self.d[i] - prev_grad[i])\n                    p.data.add_(self.d[i], alpha=-self.lr)\n\n            # Save prev_params for NEXT iteration\n            self.prev_params = curr_params\n\n            total_loss += loss.item()\n            n_batches += 1\n\n        return {\"avg_loss\": total_loss / max(n_batches, 1), \"full_grad_count\": 1}\n"
8}

Tool ResultStep 3

OK: Replaced lines 311..382 with 76 line(s) in opt-vr-bench/custom_vr.py. Editable range: 286–386.

[Current file: opt-vr-bench/custom_vr.py | editable: 286–386 | total: 505 lines]
   283| 
   284| 
   285| # ============================================================================
   286  # EDITABLE -- Variance Reduction Strategy (lines 286-370)
   287  # ============================================================================
   288  # Design a variance reduction mechanism for stochastic gradient computation.
   289  # You may modify ONLY this section.
   290  #
   291  # Interface contract:
   292  #   - VarianceReductionOptimizer.__init__(model, lr, l2_reg, loss_type, n_train, batch_size, device)
   293  #   - VarianceReductionOptimizer.train_one_epoch(X_train, y_train)
   294  #     -> trains for one epoch, returns dict with 'avg_loss'
   295  #
   296  # Available helper functions (FIXED, defined above):
   297  #   - compute_full_gradient(model, X_train, y_train, loss_typ

AssistantStep 4test

AssistantStep 1edit

Tool Input

1{
2  "content": "class VarianceReductionOptimizer:\n    \"\"\"Accelerated SARAH (SARAH with Heavy Ball Momentum).\n    \n    Combines the SARAH recursive variance reduction estimator with\n    Heavy Ball momentum to accelerate convergence across both\n    convex and non-convex finite-sum problems.\n    \"\"\"\n\n    def __init__(self, model: nn.Module, lr: float, l2_reg: float,\n                 loss_type: str, n_train: int, batch_size: int,\n                 device: torch.device):\n        self.model = model\n        self.lr = lr\n        self.l2_reg = l2_reg\n        self.loss_type = loss_type\n        self.n_train = n_train\n        self.batch_size = batch_size\n        self.device = device\n        self.params = list(model.parameters())\n        self.v = None\n        self.prev_params = None\n        self.momentum_buffer = None\n        self.beta = 0.9\n\n    def _save_params(self):\n        return [p.data.clone() for p in self.params]\n\n    def _load_params(self, saved):\n        for p, s in zip(self.params, saved):\n            p.data.copy_(s)\n\n    def train_one_epoch(self, X_train: torch.Tensor,\n                        y_train: torch.Tensor) -> dict:\n        self.model.train()\n        n = X_train.size(0)\n\n        # 1. Full gradient at the beginning of the epoch\n        full_grad = compute_full_gradient(\n            self.model, X_train, y_train, self.loss_type,\n            self.l2_reg, self.device\n        )\n        self.v = [fg.clone() for fg in full_grad]\n\n        if self.momentum_buffer is None:\n            self.momentum_buffer = [torch.zeros_like(p) for p in self.params]\n\n        indices = torch.randperm(n)\n        total_loss = 0.0\n        n_batches = 0\n\n        self.prev_params = self._save_params()\n\n        for start in range(0, n, self.batch_size):\n            end = min(start + self.batch_size, n)\n            idx = indices[start:end]\n            Xb = X_train[idx].to(self.device)\n            yb = y_train[idx].to(self.device)\n\n            # Gradient at current params\n            current_params = self._save_params()\n            self.model.zero_grad()\n            loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            loss.backward()\n            g_current = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) \n                         for p in self.params]\n\n            # Gradient at previous params\n            self._load_params(self.prev_params)\n            self.model.zero_grad()\n            prev_loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            prev_loss.backward()\n            g_prev = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) \n                      for p in self.params]\n\n            # Restore current for update\n            self._load_params(current_params)\n            self.prev_params = self._save_params()\n\n            # Variance-reduced gradient update\n            with torch.no_grad():\n                for i, p in enumerate(self.params):\n                    # SARAH estimator\n                    self.v[i] = g_current[i] - g_prev[i] + self.v[i]\n                    # Momentum\n                    self.momentum_buffer[i] = self.beta * self.momentum_buffer[i] + self.v[i]\n                    # Step\n                    p.data.add_(self.momentum_buffer[i], alpha=-self.lr)\n\n            total_loss += loss.item()\n            n_batches += 1\n\n        return {\"avg_loss\": total_loss / max(n_batches, 1), \"full_grad_count\": 1}",
3  "op": "replace",
4  "filename": "opt-vr-bench/custom_vr.py",
5  "end_line": 369,
6  "after_line": 1,
7  "start_line": 311
8}

Tool ResultStep 1

OK: Replaced lines 311..369 with 92 line(s) in opt-vr-bench/custom_vr.py. Editable range: 286–403.

[Current file: opt-vr-bench/custom_vr.py | editable: 286–403 | total: 522 lines]
   283| 
   284| 
   285| # ============================================================================
   286  # EDITABLE -- Variance Reduction Strategy (lines 286-370)
   287  # ============================================================================
   288  # Design a variance reduction mechanism for stochastic gradient computation.
   289  # You may modify ONLY this section.
   290  #
   291  # Interface contract:
   292  #   - VarianceReductionOptimizer.__init__(model, lr, l2_reg, loss_type, n_train, batch_size, device)
   293  #   - VarianceReductionOptimizer.train_one_epoch(X_train, y_train)
   294  #     -> trains for one epoch, returns dict with 'avg_loss'
   295  #
   296  # Available helper functions (FIXED, defined above):
   297  #   - compute_full_gradient(model, X_train, y_train, loss_typ

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)


## Seed 42

### logistic (scripts/logistic.sh)
Training progress (last 5 epochs):
TRAIN_METRICS: epoch=16 avg_loss=89.959705 time=1.09s grad_comps=15008
TRAIN_METRICS: epoch=17 avg_loss=102.254029 time=1.07s grad_comps=15946
TRAIN_METRICS: epoch=18 avg_loss=93.438592 time=1.06s grad_comps=16884
TRAIN_METRICS: epoch=19 avg_loss=93.777836 time=1.13s grad_comps=17822
TRAIN_METRICS: epoch=20 avg_loss=83.304029 time=1.02s grad_comps=18760
Evaluation progress (last 5 evals):
EVAL_METRICS: epoch=16 test_loss=99.825355 test_accuracy=75.400000 best_test_accuracy=85.480000
EVAL_METRICS: epoch=17 test_loss=101.720771 test_accuracy=75.720000 best_test_accuracy=85.480000
EVAL_METRICS: epoch=18 test_loss=81.406343 test_accuracy=81.040000 best_test_accuracy=85.480000
EVAL_METRICS: epoch=19 test_loss=77.526586 test_accuracy=78.930000 best_test_accuracy=85.480000
EVAL_METRICS: epoch=20 test_loss=62.801315 test_accuracy=83.160000 be

AssistantStep 3edit

Tool Input

1{
2  "after_line": 1,
3  "op": "replace",
4  "filename": "opt-vr-bench/custom_vr.py",
5  "content": "class VarianceReductionOptimizer:\n    \"\"\"SARAH with mini-epoch restarts and adaptive step size.\"\"\"\n\n    def __init__(self, model: nn.Module, lr: float, l2_reg: float,\n                 loss_type: str, n_train: int, batch_size: int,\n                 device: torch.device):\n        self.model = model\n        self.lr = lr\n        self.l2_reg = l2_reg\n        self.loss_type = loss_type\n        self.n_train = n_train\n        self.batch_size = batch_size\n        self.device = device\n        self.params = list(model.parameters())\n        self.v = None\n        self.prev_params = None\n        self.eta_max = None\n\n    def _save_params(self):\n        return [p.data.clone() for p in self.params]\n\n    def _load_params(self, saved):\n        for p, s in zip(self.params, saved):\n            p.data.copy_(s)\n\n    def _grad_norm(self, grads):\n        return math.sqrt(sum(g.pow(2).sum().item() for g in grads))\n\n    def train_one_epoch(self, X_train: torch.Tensor,\n                        y_train: torch.Tensor) -> dict:\n        self.model.train()\n        n = X_train.size(0)\n\n        # Full gradient\n        full_grad = compute_full_gradient(\n            self.model, X_train, y_train, self.loss_type,\n            self.l2_reg, self.device\n        )\n        self.v = [fg.clone() for fg in full_grad]\n        \n        gnorm = self._grad_norm(self.v)\n        pnorm = math.sqrt(sum(p.data.pow(2).sum().item() for p in self.params)) + 1e-8\n        \n        adaptive_step = 0.01 * pnorm / (gnorm + 1e-8)\n        if self.loss_type == 'mse':\n            if self.eta_max is None:\n                self.eta_max = adaptive_step\n            else:\n                self.eta_max = min(2.0 * self.eta_max, adaptive_step)\n            effective_lr = min(self.lr, adaptive_step, self.eta_max)\n        else:\n            effective_lr = min(self.lr, adaptive_step)\n\n        indices = torch.randperm(n)\n        total_loss = 0.0\n        n_batches = 0\n\n        self.prev_params = self._save_params()\n\n        for start in range(0, n, self.batch_size):\n            end = min(start + self.batch_size, n)\n            idx = indices[start:end]\n            Xb = X_train[idx].to(self.device)\n            yb = y_train[idx].to(self.device)\n\n            current_params = self._save_params()\n\n            # grad at curr\n            self.model.zero_grad()\n            loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            loss.backward()\n            g_curr = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) for p in self.params]\n\n            if start > 0:\n                # grad at prev\n                self._load_params(self.prev_params)\n                self.model.zero_grad()\n                prev_loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n                prev_loss.backward()\n                g_prev = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) for p in self.params]\n\n                # update v\n                for i in range(len(self.params)):\n                    self.v[i] = g_curr[i] - g_prev[i] + self.v[i]\n            \n            # Restore current params\n            self._load_params(current_params)\n            self.prev_params = self._save_params()\n\n            with torch.no_grad():\n                for i, p in enumerate(self.params):\n                    p.data.add_(self.v[i], alpha=-effective_lr)\n\n            total_loss += loss.item()\n            n_batches += 1\n\n        return {\"avg_loss\": total_loss / max(n_batches, 1), \"full_grad_count\": 1}",
6  "start_line": 311,
7  "end_line": 402
8}

Tool ResultStep 3

OK: Replaced lines 311..402 with 97 line(s) in opt-vr-bench/custom_vr.py. Editable range: 286–408.

[Current file: opt-vr-bench/custom_vr.py | editable: 286–408 | total: 527 lines]
   283| 
   284| 
   285| # ============================================================================
   286  # EDITABLE -- Variance Reduction Strategy (lines 286-370)
   287  # ============================================================================
   288  # Design a variance reduction mechanism for stochastic gradient computation.
   289  # You may modify ONLY this section.
   290  #
   291  # Interface contract:
   292  #   - VarianceReductionOptimizer.__init__(model, lr, l2_reg, loss_type, n_train, batch_size, device)
   293  #   - VarianceReductionOptimizer.train_one_epoch(X_train, y_train)
   294  #     -> trains for one epoch, returns dict with 'avg_loss'
   295  #
   296  # Available helper functions (FIXED, defined above):
   297  #   - compute_full_gradient(model, X_train, y_train, loss_typ

AssistantStep 4test

Tool ResultStep 4

[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### logistic (scripts/logistic.sh)
[output file not found: /srv/home/bohanlyu/MLS-Bench/logs/optimization-variance-reduction/google_gemini-3.1-pro-preview_20260405_234134/20260406_011045/group_1/logistic_s42.out]

### mlp (scripts/mlp.sh)
[output file not found: /srv/home/bohanlyu/MLS-Bench/logs/optimization-variance-reduction/google_gemini-3.1-pro-preview_20260405_234134/20260406_011045/group_1/mlp_s42.out]

### conditioned (scripts/conditioned.sh)
[output file not found: /srv/home/bohanlyu/MLS-Bench/logs/optimization-variance-reduction/google_gemini-3.1-pro-preview_20260405_234134/20260406_011045/group_1/conditioned_s42.out]

AssistantStep 5undo

Tool ResultStep 5

Undo complete:
Restored: opt-vr-bench/custom_vr.py

AssistantStep 6edit

Tool Input

1{
2  "content": "class VarianceReductionOptimizer:\n    \"\"\"SPIDER/PAGE robust estimator.\"\"\"\n\n    def __init__(self, model: nn.Module, lr: float, l2_reg: float,\n                 loss_type: str, n_train: int, batch_size: int,\n                 device: torch.device):\n        self.model = model\n        self.lr = lr\n        self.l2_reg = l2_reg\n        self.loss_type = loss_type\n        self.n_train = n_train\n        self.batch_size = batch_size\n        self.device = device\n        self.params = list(model.parameters())\n        \n        self.v = None\n        self.prev_params = None\n        \n        self.n_steps_per_epoch = max(1, n_train // batch_size)\n        self.momentum = 0.9\n\n    def _save_params(self):\n        return [p.data.clone() for p in self.params]\n\n    def _load_params(self, saved):\n        for p, s in zip(self.params, saved):\n            p.data.copy_(s)\n\n    def train_one_epoch(self, X_train: torch.Tensor,\n                        y_train: torch.Tensor) -> dict:\n        self.model.train()\n        n = X_train.size(0)\n\n        compute_full = False\n        if self.v is None:\n            compute_full = True\n            \n        full_grad_count = 0\n        if compute_full:\n            full_grad = compute_full_gradient(\n                self.model, X_train, y_train, self.loss_type,\n                self.l2_reg, self.device\n            )\n            self.v = [fg.clone() for fg in full_grad]\n            self.prev_params = self._save_params()\n            full_grad_count = 1\n\n        indices = torch.randperm(n)\n        total_loss = 0.0\n        n_batches = 0\n\n        a = 1.0 - 1.0 / math.sqrt(self.n_steps_per_epoch)\n\n        for start in range(0, n, self.batch_size):\n            end = min(start + self.batch_size, n)\n            idx = indices[start:end]\n            Xb = X_train[idx].to(self.device)\n            yb = y_train[idx].to(self.device)\n\n            current_params = self._save_params()\n\n            # g_curr\n            self.model.zero_grad()\n            loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            loss.backward()\n            g_curr = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) for p in self.params]\n\n            # g_prev\n            self._load_params(self.prev_params)\n            self.model.zero_grad()\n            prev_loss = compute_loss_on_batch(self.model, Xb, yb, self.loss_type, self.l2_reg)\n            prev_loss.backward()\n            g_prev = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) for p in self.params]\n\n            self._load_params(current_params)\n            self.prev_params = self._save_params()\n\n            with torch.no_grad():\n                for i, p in enumerate(self.params):\n                    # STORM estimator\n                    self.v[i] = g_curr[i] + a * (self.v[i] - g_prev[i])\n                    \n                    # Nesterov/Momentum-like step\n                    p.data.add_(self.v[i], alpha=-self.lr)\n\n            total_loss += loss.item()\n            n_batches += 1\n\n        return {\"avg_loss\": total_loss / max(n_batches, 1), \"full_grad_count\": full_grad_count}",
3  "start_line": 311,
4  "filename": "opt-vr-bench/custom_vr.py",
5  "end_line": 403,
6  "after_line": 1,
7  "op": "replace"
8}

Tool ResultStep 6

OK: Replaced lines 311..403 with 89 line(s) in opt-vr-bench/custom_vr.py. Editable range: 286–399.

[Current file: opt-vr-bench/custom_vr.py | editable: 286–399 | total: 518 lines]
   283| 
   284| 
   285| # ============================================================================
   286  # EDITABLE -- Variance Reduction Strategy (lines 286-370)
   287  # ============================================================================
   288  # Design a variance reduction mechanism for stochastic gradient computation.
   289  # You may modify ONLY this section.
   290  #
   291  # Interface contract:
   292  #   - VarianceReductionOptimizer.__init__(model, lr, l2_reg, loss_type, n_train, batch_size, device)
   293  #   - VarianceReductionOptimizer.train_one_epoch(X_train, y_train)
   294  #     -> trains for one epoch, returns dict with 'avg_loss'
   295  #
   296  # Available helper functions (FIXED, defined above):
   297  #   - compute_full_gradient(model, X_train, y_train, loss_typ