Agent Conversation: google/gemini-3.1-pro-preview

Model: google/gemini-3.1-pro-preview
Total steps: 6
Messages: 19
UserStep 0

Task: optimization-bilevel

Optimization Bilevel

Research Question

Can you improve a fixed bilevel-optimization benchmark based on Shen and Chen's penalty-based bilevel gradient descent experiments by selecting a better supported method and tuning only paper-style strategy hyperparameters?

What You Can Modify

Edit only penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py inside the editable block containing:

  1. get_toy_strategy()
  2. get_hyperclean_strategy(net)

These functions may only choose among the supported methods already implemented in the fixed driver:

  • Toy mode: v_pbgd, g_pbgd
  • Data hyper-cleaning mode: v_pbgd, g_pbgd, rhg, t_rhg

You should only change strategy-level choices already present in the paper/codebase, such as:

  • method selection
  • learning rates
  • penalty schedule (gamma_init, gamma_max, gamma_argmax_step)
  • inner / outer iteration counts
  • RHG truncation depth (K) and inner-loop length (T)

Do not rewrite the driver, dataset split, pollution protocol, metrics, or model architectures.

Fixed Setup

Toy / Numerical Verification

  • Problem definition follows Section 5.1 / 6.1 of the paper
  • x is projected to [0, 3]
  • 1000 random initial points are sampled as in the official toy script
  • Primary metric: convergence_steps
  • Secondary metrics: success_rate, final_residual, runtime_sec

Data Hyper-Cleaning

  • MNIST split: 5000 train / 5000 validation / 10000 test
  • Pollution rate: 50%
  • Pollution logic follows the released official code
  • Models: linear classifier and 2-layer MLP (784 -> 300 -> 10, sigmoid hidden layer)
  • Primary metric: test_accuracy
  • Secondary metrics: f1_score, cleaner precision / recall, runtime to best accuracy

Reference Files

The following official source files are provided read-only for fidelity:

  • penalized-bilevel-gradient-descent/V-PBGD/toy/toy.py
  • penalized-bilevel-gradient-descent/V-PBGD/data-hyper-cleaning/data_hyper_clean.py
  • penalized-bilevel-gradient-descent/G-PBGD/data_hyper_clean_gpbgd.py
  • penalized-bilevel-gradient-descent/RHG/data_hyper_clean_rhg.py
  • penalized-bilevel-gradient-descent/RHG/hypergrad/hypergradients.py

Evaluation

The task runs three benchmark commands:

  1. toy-convergence
  2. hyperclean-linear
  3. hyperclean-mlp

Each command prints structured TRAIN_METRICS and FINAL_METRICS lines. The parser records the final metrics separately for each command label.

Hints

  • The paper's official hyperparameters are already a strong starting point for V-PBGD.
  • G-PBGD is competitive but can be sensitive to the penalty schedule.
  • RHG and T-RHG use a different hypergradient pipeline; K and T matter more than gamma.
  • Improving toy convergence by shrinking gamma too aggressively can hurt lower-level feasibility, which the benchmark also records.

penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py [EDITABLE — lines 216–253 only]

     1: """Optim
AssistantStep 1edit
Tool Input
1{
2  "filename": "penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py",
3  "op": "replace",
4  "end_line": 253,
5  "after_line": 1,
6  "start_line": 216,
7  "content": "def get_toy_strategy() -> ToyStrategy:\n    return ToyStrategy(\n        method=\"v_pbgd\",\n        gams=(2.0,),\n        alpha0=0.04,\n    )\n\n\ndef get_hyperclean_strategy(net: str) -> HypercleanStrategy:\n    if net == \"linear\":\n        return HypercleanStrategy(\n            method=\"v_pbgd\",\n            lrx=0.2,\n            lry=0.2,\n            lr_inner=0.02,\n            gamma_init=0.0,\n            gamma_max=0.5,\n            gamma_argmax_step=40_000,\n            outer_itr=50_000,\n            inner_itr=1,\n            reg=0.0,\n            eval_interval=10,\n        )\n    if net == \"mlp\":\n        return HypercleanStrategy(\n            method=\"g_pbgd\",\n            lrx=0.6,\n            lry=0.6,\n            gamma_init=0.0,\n            gamma_max=40.0,\n            gamma_argmax_step=40_000,\n            outer_itr=60_000,\n            reg=0.0,\n            eval_interval=10,\n        )\n    raise ValueError(f\"Unsupported network: {net}\")"
8}
Tool ResultStep 1
OK: Replaced lines 216..253 with 36 line(s) in penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py. Editable range: 216–251.

[Current file: penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py | editable: 216–251 | total: 930 lines]
   213| # =====================================================================
   214| # EDITABLE: select supported methods and paper-style hyperparameters only
   215| # =====================================================================
   216  def get_toy_strategy() -> ToyStrategy:
   217      return ToyStrategy(
   218          method="v_pbgd",
   219          gams=(2.0,),
   220          alpha0=0.04,
   221      )
   222  
   223  
   224  def get_hyperclean_strategy(net: str) -> HypercleanStrategy:
   225      if net == "linear":
   226          return HypercleanStrategy(
   227              method="v_pbgd",
   228              lrx=0.2,
   229              lry=0.2,
   230              lr_inner=0.02,
   231              gamma_i
AssistantStep 2test
AssistantStep 1edit
Tool Input
1{
2  "end_line": 253,
3  "filename": "penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py",
4  "content": "def get_toy_strategy() -> ToyStrategy:\n    return ToyStrategy(\n        method=\"v_pbgd\",\n        gams=(10.0,),\n        alpha0=0.2, # increased\n    )\n\n\ndef get_hyperclean_strategy(net: str) -> HypercleanStrategy:\n    if net == \"linear\":\n        return HypercleanStrategy(\n            method=\"v_pbgd\",\n            lrx=0.2,\n            lry=0.2,\n            lr_inner=0.1,\n            gamma_init=0.0,\n            gamma_max=0.3,\n            gamma_argmax_step=20_000,\n            outer_itr=20_000,\n            inner_itr=1,\n            reg=0.0,\n            eval_interval=10,\n        )\n    if net == \"mlp\":\n        return HypercleanStrategy(\n            method=\"g_pbgd\",\n            lrx=0.5,\n            lry=0.5,\n            gamma_init=0.0,\n            gamma_max=37.0,\n            gamma_argmax_step=30_000,\n            outer_itr=50_000,\n            reg=0.0,\n            eval_interval=10,\n        )\n    raise ValueError(f\"Unsupported network: {net}\")\n",
5  "op": "replace",
6  "after_line": 214,
7  "start_line": 216
8}
Tool ResultStep 1
OK: Replaced lines 216..253 with 36 line(s) in penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py. Editable range: 216–251.

[Current file: penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py | editable: 216–251 | total: 930 lines]
   213| # =====================================================================
   214| # EDITABLE: select supported methods and paper-style hyperparameters only
   215| # =====================================================================
   216  def get_toy_strategy() -> ToyStrategy:
   217      return ToyStrategy(
   218          method="v_pbgd",
   219          gams=(10.0,),
   220          alpha0=0.2, # increased
   221      )
   222  
   223  
   224  def get_hyperclean_strategy(net: str) -> HypercleanStrategy:
   225      if net == "linear":
   226          return HypercleanStrategy(
   227              method="v_pbgd",
   228              lrx=0.2,
   229              lry=0.2,
   230              lr_inner=0.1,
   231
AssistantStep 2test
AssistantStep 1edit
Tool Input
1{
2  "filename": "penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py",
3  "end_line": 253,
4  "start_line": 216,
5  "content": "def get_toy_strategy() -> ToyStrategy:\n    return ToyStrategy(\n        method=\"v_pbgd\",\n        gams=(10.0,),\n        alpha0=0.2,\n    )\n\n\ndef get_hyperclean_strategy(net: str) -> HypercleanStrategy:\n    if net == \"linear\":\n        return HypercleanStrategy(\n            method=\"v_pbgd\",\n            lrx=0.2,\n            lry=0.2,\n            lr_inner=0.01,\n            gamma_init=0.0,\n            gamma_max=0.2,\n            gamma_argmax_step=30_000,\n            outer_itr=40_000,\n            inner_itr=1,\n            reg=0.0,\n            eval_interval=10,\n        )\n    if net == \"mlp\":\n        return HypercleanStrategy(\n            method=\"g_pbgd\",\n            lrx=0.5,\n            lry=0.5,\n            gamma_init=0.0,\n            gamma_max=40.0,\n            gamma_argmax_step=30_000,\n            outer_itr=50_000,\n            reg=0.0,\n            eval_interval=10,\n        )\n    raise ValueError(f\"Unsupported network: {net}\")",
6  "op": "replace",
7  "after_line": 215
8}
Tool ResultStep 1
OK: Replaced lines 216..253 with 36 line(s) in penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py. Editable range: 216–251.

[Current file: penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py | editable: 216–251 | total: 930 lines]
   213| # =====================================================================
   214| # EDITABLE: select supported methods and paper-style hyperparameters only
   215| # =====================================================================
   216  def get_toy_strategy() -> ToyStrategy:
   217      return ToyStrategy(
   218          method="v_pbgd",
   219          gams=(10.0,),
   220          alpha0=0.2,
   221      )
   222  
   223  
   224  def get_hyperclean_strategy(net: str) -> HypercleanStrategy:
   225      if net == "linear":
   226          return HypercleanStrategy(
   227              method="v_pbgd",
   228              lrx=0.2,
   229              lry=0.2,
   230              lr_inner=0.01,
   231              gamma_i
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### toy-convergence (scripts/toy_convergence.sh)
Training progress:
TRAIN_METRICS run=995 step=150 objective=0.423728 residual=0.038709 projected_grad=0.000010 success=1
TRAIN_METRICS run=996 step=152 objective=0.423728 residual=0.038709 projected_grad=0.000010 success=1
TRAIN_METRICS run=997 step=136 objective=0.174725 residual=0.023824 projected_grad=0.000010 success=1
TRAIN_METRICS run=998 step=151 objective=0.423728 residual=0.038709 projected_grad=0.000010 success=1
TRAIN_METRICS run=999 step=104 objective=0.174724 residual=0.023824 projected_grad=0.000009 success=1

Final metrics:
FINAL_METRICS convergence_steps=147.298000 median_steps=151.000000 final_residual=0.033321 final_projected_grad=0.000010 success_rate=1.000000 runtime_sec=0.312483 score=147.298000

### hyperclean-linear (scripts/hyperclean_linear.sh)
Training progress:
TRAIN_METRICS step=39950 train_loss=1.352762 val_loss=0.145256 test_accuracy=87.4
AssistantStep 3edit
Tool Input
1{
2  "content": "def get_toy_strategy() -> ToyStrategy:\n    return ToyStrategy(\n        method=\"v_pbgd\",\n        gams=(5.0, 10.0),\n        alpha0=0.25,\n    )\n\n\ndef get_hyperclean_strategy(net: str) -> HypercleanStrategy:\n    if net == \"linear\":\n        return HypercleanStrategy(\n            method=\"v_pbgd\",\n            lrx=1.0,\n            lry=0.1,\n            lr_inner=0.01,\n            gamma_init=0.0,\n            gamma_max=0.5,\n            gamma_argmax_step=20_000,\n            outer_itr=40_000,\n            inner_itr=1,\n            reg=0.0,\n            eval_interval=10,\n        )\n    if net == \"mlp\":\n        return HypercleanStrategy(\n            method=\"g_pbgd\",\n            lrx=1.0,\n            lry=0.5,\n            gamma_init=0.0,\n            gamma_max=50.0,\n            gamma_argmax_step=40_000,\n            outer_itr=60_000,\n            reg=0.0,\n            eval_interval=10,\n        )\n    raise ValueError(f\"Unsupported network: {net}\")",
3  "filename": "penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py",
4  "after_line": 215,
5  "start_line": 216,
6  "op": "replace",
7  "end_line": 251
8}
Tool ResultStep 3
OK: Replaced lines 216..251 with 36 line(s) in penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py. Editable range: 216–251.

[Current file: penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py | editable: 216–251 | total: 930 lines]
   213| # =====================================================================
   214| # EDITABLE: select supported methods and paper-style hyperparameters only
   215| # =====================================================================
   216  def get_toy_strategy() -> ToyStrategy:
   217      return ToyStrategy(
   218          method="v_pbgd",
   219          gams=(5.0, 10.0),
   220          alpha0=0.25,
   221      )
   222  
   223  
   224  def get_hyperclean_strategy(net: str) -> HypercleanStrategy:
   225      if net == "linear":
   226          return HypercleanStrategy(
   227              method="v_pbgd",
   228              lrx=1.0,
   229              lry=0.1,
   230              lr_inner=0.01,
   231              ga
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### toy-convergence (scripts/toy_convergence.sh)
Training progress:
TRAIN_METRICS run=995 step=54 objective=0.367849 residual=0.087143 projected_grad=0.000010 success=1
TRAIN_METRICS run=996 step=56 objective=0.367849 residual=0.087143 projected_grad=0.000008 success=1
TRAIN_METRICS run=997 step=40000 objective=1.044433 residual=0.507769 projected_grad=10.479585 success=0
TRAIN_METRICS run=998 step=55 objective=0.367849 residual=0.087143 projected_grad=0.000008 success=1
TRAIN_METRICS run=999 step=40000 objective=-0.356360 residual=0.227072 projected_grad=10.479585 success=0

Final metrics:
FINAL_METRICS convergence_steps=14713.669000 median_steps=56.000000 final_residual=0.183128 final_projected_grad=3.846013 success_rate=0.633000 runtime_sec=14.882439 score=14713.669000

### hyperclean-linear (scripts/hyperclean_linear.sh)
[output file not found: /srv/home/bohanlyu/MLS-Bench/logs/optimization-bilevel/google_gemini-
AssistantStep 5edit
Tool Input
1{
2  "after_line": 215,
3  "start_line": 216,
4  "filename": "penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py",
5  "content": "def get_toy_strategy() -> ToyStrategy:\n    return ToyStrategy(\n        method=\"v_pbgd\",\n        gams=(5.0,),\n        alpha0=0.15,\n    )\n\n\ndef get_hyperclean_strategy(net: str) -> HypercleanStrategy:\n    if net == \"linear\":\n        return HypercleanStrategy(\n            method=\"g_pbgd\",\n            lrx=0.5,\n            lry=0.5,\n            gamma_init=0.0,\n            gamma_max=10.0,\n            gamma_argmax_step=10_000,\n            outer_itr=40_000,\n            reg=0.0,\n            eval_interval=10,\n        )\n    if net == \"mlp\":\n        return HypercleanStrategy(\n            method=\"g_pbgd\",\n            lrx=0.5,\n            lry=0.5,\n            gamma_init=0.0,\n            gamma_max=10.0,\n            gamma_argmax_step=15_000,\n            outer_itr=40_000,\n            reg=0.0,\n            eval_interval=10,\n        )\n    raise ValueError(f\"Unsupported network: {net}\")",
6  "end_line": 251,
7  "op": "replace"
8}
Tool ResultStep 5
OK: Replaced lines 216..251 with 34 line(s) in penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py. Editable range: 216–249.

[Current file: penalized-bilevel-gradient-descent/mlsbench/custom_strategy.py | editable: 216–249 | total: 928 lines]
   213| # =====================================================================
   214| # EDITABLE: select supported methods and paper-style hyperparameters only
   215| # =====================================================================
   216  def get_toy_strategy() -> ToyStrategy:
   217      return ToyStrategy(
   218          method="v_pbgd",
   219          gams=(5.0,),
   220          alpha0=0.15,
   221      )
   222  
   223  
   224  def get_hyperclean_strategy(net: str) -> HypercleanStrategy:
   225      if net == "linear":
   226          return HypercleanStrategy(
   227              method="g_pbgd",
   228              lrx=0.5,
   229              lry=0.5,
   230              gamma_init=0.0,
   231              gamma_
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining)

### toy-convergence (scripts/toy_convergence.sh)
Training progress:
TRAIN_METRICS run=995 step=95 objective=0.367849 residual=0.087143 projected_grad=0.000009 success=1
TRAIN_METRICS run=996 step=98 objective=0.367849 residual=0.087143 projected_grad=0.000009 success=1
TRAIN_METRICS run=997 step=75 objective=0.147460 residual=0.049164 projected_grad=0.000009 success=1
TRAIN_METRICS run=998 step=96 objective=0.367849 residual=0.087143 projected_grad=0.000010 success=1
TRAIN_METRICS run=999 step=72 objective=0.147459 residual=0.049165 projected_grad=0.000009 success=1

Final metrics:
FINAL_METRICS convergence_steps=3374.788000 median_steps=96.000000 final_residual=0.142290 final_projected_grad=3.663069 success_rate=0.835000 runtime_sec=6.477770 score=3374.788000

### hyperclean-linear (scripts/hyperclean_linear.sh)
[output file not found: /srv/home/bohanlyu/MLS-Bench/logs/optimization-bilevel/google_gemini-3.1-pro-preview_20260405_233834/20260406_035811/gro