Agent Conversation: gemini-3.1-pro-preview

Model: gemini-3.1-pro-preview

Total steps: 7

Messages: 16

UserStep 0

Task: llm-pretrain-optimizer

LLM Pretraining: Optimizer & Learning Rate Schedule Optimization

Research Question

Design an improved optimizer and/or learning rate schedule for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard AdamW with cosine annealing schedule.

What You Can Modify

Two regions in custom_pretrain.py:

configure_optimizers method (lines 172-189): Optimizer creation and parameter grouping
get_lr function (lines 192-201): Learning rate schedule

You can modify:

The optimization algorithm (default: AdamW with fused implementation)
Parameter grouping strategy (default: weight decay for 2D params, no decay for 1D)
Learning rate schedule shape (default: cosine with linear warmup)
Any optimizer hyperparameters

Note: The training loop calls get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr) — keep this signature compatible. The optimizer returned by configure_optimizers must support .zero_grad(), .step(), and .param_groups.

Evaluation

Metric: Validation loss (cross-entropy, lower is better), plus perplexity (WikiText-2, LAMBADA) and downstream accuracy (ARC-Easy, HellaSwag, PIQA, WinoGrande)
Model: GPT-2 Medium (24L/16H/1024D, ~355M params)
Dataset: FineWeb 10B (GPT-2 tokenizer), ~7.1B tokens (D=20N Chinchilla-optimal)
Training: 12030 iterations, BSZ=96, GA=6, 2-GPU DDP
Hardware: H200 GPU

nanoGPT/custom_pretrain.py [EDITABLE — lines 171–189, lines 245–247 only]

     1: """Custom GPT-2 Pretraining Script
     2: Based on Andrej Karpathy's nanoGPT, evaluated on FineWeb dataset.
     3: """
     4: 
     5: import math
     6: import inspect
     7: import os
     8: import time
     9: from contextlib import nullcontext
    10: from dataclasses import dataclass
    11: 
    12: import numpy as np
    13: import torch
    14: import torch.nn as nn
    15: from torch.nn import functional as F
    16: 
    17: # ============================================================================
    18: # Model Components
    19: # ============================================================================
    20: 
    21: # ── Normalization ──────────────────────────────────────────────────────────
    22: class LayerNorm(nn.Module):
    23:     """LayerNorm but with an optional bias."""
    24:     def __init__(self, ndim, bias):
    25:         super().__init__()
    26:         self.weight = nn.Parameter(torch.ones(ndim))
    27:         self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
    28: 
    29:     def forward(self, input):
    30:         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31: 
    32: # ── Self-Attention ─────────────────────────────────────────────────────────
    33: class CausalSelfAttention(nn.Module):
    34:     def __init__(self, config):
    35:         super().__init__()
    36:         assert config.n

AssistantStep 1edit

Tool Input

1{
2  "end_line": 247,
3  "op": "replace",
4  "filename": "nanoGPT/custom_pretrain.py",
5  "content": "    CONFIG_OVERRIDES = {'learning_rate': 1e-3, 'weight_decay': 0.1, 'warmup_iters': 1000}",
6  "start_line": 245
7}

Tool ResultStep 1

OK: Replaced lines 245..247 with 1 line(s) in nanoGPT/custom_pretrain.py. Editable range: 171–189, 245–245.

[Current file: nanoGPT/custom_pretrain.py | editable: 171–189, 245–245 | total: 435 lines]
   171      def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
   172          param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
   173          decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
       ... (13 more lines) ...
   187          print(f"using fused AdamW: {use_fused}")
   188          return optimizer
   189  
...
   245      CONFIG_OVERRIDES = {'learning_rate': 1e-3, 'weight_decay': 0.1, 'warmup_iters': 1000}

AssistantStep 2edit

Tool Input

1{
2  "start_line": 171,
3  "filename": "nanoGPT/custom_pretrain.py",
4  "end_line": 188,
5  "op": "replace",
6  "content": "    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):\n        param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}\n        decay_params = [(n, p) for n, p in param_dict.items() if p.dim() >= 2]\n        nodecay_params = [(n, p) for n, p in param_dict.items() if p.dim() < 2]\n\n        muon_params = [p for n, p in decay_params if 'wte' not in n and 'wpe' not in n and 'lm_head' not in n]\n        adam_decay_params = [p for n, p in decay_params if 'wte' in n or 'wpe' in n or 'lm_head' in n]\n        adam_nodecay_params = [p for _, p in nodecay_params]\n\n        class Muon(torch.optim.Optimizer):\n            def __init__(self, params, lr=0.02, momentum=0.95, ns_steps=5, weight_decay=0.0):\n                super().__init__(params, dict(lr=lr, momentum=momentum, ns_steps=ns_steps, weight_decay=weight_decay))\n            @staticmethod\n            def zeroth_power_via_newtonschulz5(G, steps):\n                X = G.bfloat16()\n                X = X / (X.norm() + 1e-7)\n                if G.size(0) > G.size(1): X = X.T\n                a, b, c = 3.4445, -4.7750, 2.0315\n                for _ in range(steps):\n                    A = X @ X.T\n                    X = a * X + b * (A @ X) + c * (A @ (A @ X))\n                if G.size(0) > G.size(1): X = X.T\n                return X\n            @torch.no_grad()\n            def step(self):\n                for group in self.param_groups:\n                    for p in group['params']:\n                        if p.grad is None: continue\n                        if group['weight_decay'] > 0: p.mul_(1 - group['lr'] * group['weight_decay'])\n                        g = p.grad\n                        state = self.state[p]\n                        if 'momentum_buffer' not in state: state['momentum_buffer'] = torch.zeros_like(g)\n                        buf = state['momentum_buffer']\n                        buf.lerp_(g, 1.0 - group['momentum'])\n                        nesterov_g = g.lerp(buf, group['momentum'])\n                        if nesterov_g.dim() == 2:\n                            orig_shape = nesterov_g.shape\n                            if orig_shape[0] == 3 * orig_shape[1]:\n                                update = torch.cat([self.zeroth_power_via_newtonschulz5(part, group['ns_steps']) for part in nesterov_g.split(orig_shape[1])])\n                                scale = max(1, orig_shape[0] // orig_shape[1]) ** 0.5\n                            else:\n                                update = self.zeroth_power_via_newtonschulz5(nesterov_g, group['ns_steps'])\n                                scale = max(1, orig_shape[0] / orig_shape[1]) ** 0.5\n                            p.data.add_(update.to(p.dtype), alpha=-group['lr'] * scale)\n                        else:\n                            p.add_(buf, alpha=-group['lr'])\n\n        muon_lr = 0.02\n        muon_opt = Muon([{'params': muon_params, 'lr_scale': muon_lr / learning_rate}], lr=muon_lr, weight_decay=0.1)\n        adam_opt = torch.optim.NAdam([\n            {'params': adam_decay_params, 'weight_decay': weight_decay},\n            {'params': adam_nodecay_params, 'weight_decay': 0.0}\n        ], lr=learning_rate, betas=betas, decoupled_weight_decay=True)\n\n        class CombinedOptimizer:\n            def __init__(self, optimizers):\n                self.optimizers = optimizers\n                self.param_groups = [g for opt in optimizers for g in opt.param_groups]\n            def zero_grad(self, set_to_none=True):\n                for opt in self.optimizers: opt.zero_grad(set_to_none=set_to_none)\n            def step(self):\n                for opt in self.optimizers: opt.step()\n        return CombinedOptimizer([muon_opt, adam_opt])"
7}

Tool ResultStep 2

OK: Replaced lines 171..188 with 63 line(s) in nanoGPT/custom_pretrain.py. Editable range: 171–234, 290–290.

[Current file: nanoGPT/custom_pretrain.py | editable: 171–234, 290–290 | total: 480 lines]
   171      def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
   172          param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
   173          decay_params = [(n, p) for n, p in param_dict.items() if p.dim() >= 2]
       ... (58 more lines) ...
   232                  for opt in self.optimizers: opt.step()
   233          return CombinedOptimizer([muon_opt, adam_opt])
   234  
...
   290      CONFIG_OVERRIDES = {'learning_rate': 1e-3, 'weight_decay': 0.1, 'warmup_iters': 1000}

AssistantStep 3test

Tool ResultStep 3

[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6850, val_loss=2.3941
TRAIN_METRICS: step=9000, train_loss=2.6151, val_loss=2.3282
TRAIN_METRICS: step=10000, train_loss=2.5669, val_loss=2.2777
TRAIN_METRICS: step=11000, train_loss=2.5130, val_loss=2.2222
TRAIN_METRICS: step=12000, train_loss=2.4932, val_loss=2.2043
Final metrics (gpt-345m): val_loss_gpt-345m=2.1978, wikitext2_ppl_gpt-345m=38.2000, lambada_ppl_gpt-345m=59.7100

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=59.4700, hellaswag_lm-eval-345m=36.7700, piqa_lm-eval-345m=65.1300, winogrande_lm-eval-345m=51.6200

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.1978, 'wikitext2_ppl_gpt-345m': 38.2, 'lambada_ppl_gpt-345m': 59.71, 'elapsed_gpt-345m': 21258, 'arc_easy_lm-eval-345m': 59.47, 'hellaswag_lm-eval-345m': 36.77, 'piqa_

AssistantStep 4edit

Tool Input

1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "end_line": 233,
4  "start_line": 171,
5  "op": "replace",
6  "content": "    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):\n        param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}\n        decay_params = [(n, p) for n, p in param_dict.items() if p.dim() >= 2]\n        nodecay_params = [(n, p) for n, p in param_dict.items() if p.dim() < 2]\n\n        muon_params = [p for n, p in decay_params if 'wte' not in n and 'wpe' not in n and 'lm_head' not in n]\n        adam_decay_params = [p for n, p in decay_params if 'wte' in n or 'wpe' in n or 'lm_head' in n]\n        adam_nodecay_params = [p for _, p in nodecay_params]\n\n        class Muon(torch.optim.Optimizer):\n            def __init__(self, params, lr=0.02, momentum=0.95, ns_steps=5, weight_decay=0.0):\n                super().__init__(params, dict(lr=lr, momentum=momentum, ns_steps=ns_steps, weight_decay=weight_decay))\n            @staticmethod\n            def zeroth_power_via_newtonschulz5(G, steps):\n                X = G.bfloat16()\n                X = X / (X.norm() + 1e-7)\n                if G.size(0) > G.size(1): X = X.T\n                a, b, c = 3.4445, -4.7750, 2.0315\n                for _ in range(steps):\n                    A = X @ X.T\n                    X = a * X + b * (A @ X) + c * (A @ (A @ X))\n                if G.size(0) > G.size(1): X = X.T\n                return X\n            @torch.no_grad()\n            def step(self):\n                for group in self.param_groups:\n                    for p in group['params']:\n                        if p.grad is None: continue\n                        if group['weight_decay'] > 0: p.mul_(1 - group['lr'] * group['weight_decay'])\n                        g = p.grad\n                        state = self.state[p]\n                        if 'momentum_buffer' not in state: state['momentum_buffer'] = torch.zeros_like(g)\n                        buf = state['momentum_buffer']\n                        buf.lerp_(g, 1.0 - group['momentum'])\n                        nesterov_g = g.lerp(buf, group['momentum'])\n                        if nesterov_g.dim() == 2:\n                            orig_shape = nesterov_g.shape\n                            if orig_shape[0] == 3 * orig_shape[1]:\n                                update = torch.cat([self.zeroth_power_via_newtonschulz5(part, group['ns_steps']) for part in nesterov_g.split(orig_shape[1])])\n                                scale = max(1, orig_shape[0] // orig_shape[1]) ** 0.5\n                            else:\n                                update = self.zeroth_power_via_newtonschulz5(nesterov_g, group['ns_steps'])\n                                scale = max(1, orig_shape[0] / orig_shape[1]) ** 0.5\n                            p.data.add_(update.to(p.dtype), alpha=-group['lr'] * scale)\n                        else:\n                            p.add_(buf, alpha=-group['lr'])\n\n        muon_lr = 0.02\n        muon_opt = Muon([{'params': muon_params, 'lr_scale': muon_lr / learning_rate}], lr=muon_lr, weight_decay=0.1)\n        adam_opt = torch.optim.NAdam([\n            {'params': adam_decay_params, 'weight_decay': weight_decay},\n            {'params': adam_nodecay_params, 'weight_decay': 0.0}\n        ], lr=learning_rate, betas=betas, decoupled_weight_decay=True)\n\n        class CombinedOptimizer:\n            def __init__(self, optimizers):\n                self.optimizers = optimizers\n                self.param_groups = [g for opt in optimizers for g in opt.param_groups]\n                self.step_count = 0\n                self.max_iters = 12030\n                self.warmup_iters = 1000\n                self.decay_iters = 2400\n                self.stable_iters = self.max_iters - self.decay_iters\n                self.base_lr = learning_rate\n                self.min_lr = 0.0\n            def zero_grad(self, set_to_none=True):\n                for opt in self.optimizers: opt.zero_grad(set_to_none=set_to_none)\n            def step(self):\n                self.step_count += 1\n                it = self.step_count - 1\n                if it < self.warmup_iters:\n                    lr = self.base_lr * (it + 1) / (self.warmup_iters + 1)\n                elif it < self.stable_iters:\n                    lr = self.base_lr\n                else:\n                    decay_ratio = (it - self.stable_iters) / self.decay_iters\n                    decay_ratio = min(1.0, max(0.0, decay_ratio))\n                    lr = self.min_lr + 0.5 * (self.base_lr - self.min_lr) * (1.0 + math.cos(math.pi * decay_ratio))\n                for opt in self.optimizers:\n                    for group in opt.param_groups:\n                        group['lr'] = lr * group.get('lr_scale', 1.0)\n                for opt in self.optimizers: opt.step()\n        return CombinedOptimizer([muon_opt, adam_opt])"
7}

Tool ResultStep 4

OK: Replaced lines 171..233 with 83 line(s) in nanoGPT/custom_pretrain.py. Editable range: 171–254, 310–310.

[Current file: nanoGPT/custom_pretrain.py | editable: 171–254, 310–310 | total: 500 lines]
   171      def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
   172          param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
   173          decay_params = [(n, p) for n, p in param_dict.items() if p.dim() >= 2]
       ... (78 more lines) ...
   252                  for opt in self.optimizers: opt.step()
   253          return CombinedOptimizer([muon_opt, adam_opt])
   254  
...
   310      CONFIG_OVERRIDES = {'learning_rate': 1e-3, 'weight_decay': 0.1, 'warmup_iters': 1000}

AssistantStep 5test

Tool ResultStep 5

[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.8908, val_loss=2.6078
TRAIN_METRICS: step=9000, train_loss=2.8797, val_loss=2.5943
TRAIN_METRICS: step=10000, train_loss=2.8630, val_loss=2.5714
TRAIN_METRICS: step=11000, train_loss=2.6771, val_loss=2.3797
TRAIN_METRICS: step=12000, train_loss=2.5255, val_loss=2.2283
Final metrics (gpt-345m): val_loss_gpt-345m=2.2217, wikitext2_ppl_gpt-345m=39.1100, lambada_ppl_gpt-345m=62.3400

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=60.1000, hellaswag_lm-eval-345m=35.4100, piqa_lm-eval-345m=64.9600, winogrande_lm-eval-345m=50.7500

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2217, 'wikitext2_ppl_gpt-345m': 39.11, 'lambada_ppl_gpt-345m': 62.34, 'elapsed_gpt-345m': 21079, 'arc_easy_lm-eval-345m': 60.1, 'hellaswag_lm-eval-345m': 35.41, 'piqa_l

AssistantStep 6undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 6

Undo complete:
Restored: nanoGPT/custom_pretrain.py

AssistantStep 7submit

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 7

[submit] Submitting result from test #1 as final.

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6850, val_loss=2.3941
TRAIN_METRICS: step=9000, train_loss=2.6151, val_loss=2.3282
TRAIN_METRICS: step=10000, train_loss=2.5669, val_loss=2.2777
TRAIN_METRICS: step=11000, train_loss=2.5130, val_loss=2.2222
TRAIN_METRICS: step=12000, train_loss=2.4932, val_loss=2.2043
Final metrics (gpt-345m): val_loss_gpt-345m=2.1978, wikitext2_ppl_gpt-345m=38.2000, lambada_ppl_gpt-345m=59.7100

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=59.4700, hellaswag_lm-eval-345m=36.7700, piqa_lm-eval-345m=65.1300, winogrande_lm-eval-345m=51.6200

[submit] Finalized test #1 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.1978, 'wikitext2_ppl_gpt-345m': 38.2, 'lambada_ppl_gpt-345m': 59.71, 'elapsed_gpt-345m': 21258, 'arc_easy_lm-eval-345m': 59.47, 'hellaswag_lm-eval-345m': 36.77, 'piqa_lm-eval