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6e3f5f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | # Intervention Sweep Runs
This doc covers the experiment scripts for the intervention sweep experiments.
## Scripts Overview
| Script | Purpose | Variables |
|--------|---------|-----------|
| `run_top_p_sweep.sh` | Sweep RV-filter strength | `rollout_filter_value` (`1.0,0.98,0.95,0.9,0.8,0.6,0.4,nofilter`) |
| `run_kl_sweep.sh` | Sweep KL regularization | `kl_loss_coef` (`0,0.001,0.003,0.01,0.03,0.1`) |
| `run_entropy_sweep.sh` | Sweep entropy regularization | `entropy_coeff` (`0,0.001,0.003,0.01,0.03,0.1`) |
All three scripts run Sokoban with `Qwen2.5-3B`, `GAE`.
---
## 1. Top-p Sweep (`run_top_p_sweep.sh`)
Scans `actor_rollout_ref.rollout.rollout_filter_value` on Sokoban.
Goal:
- Isolate the effect of RV-filter strength while keeping KL and entropy lightly enabled at `0.001`
Key Details:
- Reward-variance filtering scores each env group by the standard deviation of rollout rewards within the group
- Selection uses `top_p`, `largest`, `reward_variance`, and explicitly fixes `rollout_filter_top_p_prob_mode=softmax`
- Filtered groups are dropped as whole groups, and this sweep keeps `filter_loss_scaling=none`
- `1.0` and `nofilter` are different conditions: `1.0` still uses `include_zero=False`, while `nofilter` sets `include_zero=True`
Options:
- `--steps` (default: `400`)
- `--rollout_filter_value` (comma list; default: `1.0,0.98,0.95,0.9,0.8,0.6,0.4,nofilter`)
- `--gpus` (comma list; auto-detect if omitted)
- `--gpus-per-exp` (default: `1`)
- `--ray-num-cpus` (default: `16`)
- `--gpu-memory-utilization` (default: `0.5`)
- `--save-freq` (default: `-1`)
Examples:
```bash
# Run the full default sweep
bash scripts/runs/run_top_p_sweep.sh
# Run one `0.9` point and one `nofilter` point on 4xH100 each
bash scripts/runs/run_top_p_sweep.sh --rollout_filter_value 0.9,nofilter --gpus-per-exp 4 --gpus 0,1,2,3,4,5,6,7
```
Outputs:
- Per-value logs: `logs/top_p_sweep_Qwen2.5-3B/<value_label>/`
- Summary log: `logs/top_p_sweep_Qwen2.5-3B.log`
---
## 2. KL Sweep (`run_kl_sweep.sh`)
Scans `actor_rollout_ref.actor.kl_loss_coef` on Sokoban.
Goal:
- Isolate the effect of KL regularization while fixing entropy to `0` and keeping RV-filter effectively off with `rollout_filter_value=1`
Key Details:
- KL is computed token-wise between the current policy and a frozen reference policy worker
- This sweep uses the actor KL loss, not reward-level KL shaping
- When `kl_loss_coef=0`, the script also sets `use_kl_loss=False`, so the ref-policy forward pass is skipped
- Increasing `kl_loss_coef` penalizes drift from the reference policy more strongly
Options:
- `--steps` (default: `400`)
- `--kl-values` (comma list; default: `0,0.001,0.003,0.01,0.03,0.1`)
- `--rollout_filter_include_zero` (bool; default: `True`)
- `--gpus` (comma list; auto-detect if omitted)
- `--gpus-per-exp` (default: `1`)
- `--ray-num-cpus` (default: `16`)
- `--gpu-memory-utilization` (default: `0.5`)
- `--save-freq` (default: `-1`)
Examples:
```bash
# Run the full default sweep
bash scripts/runs/run_kl_sweep.sh
# Run two KL points on 4xH100 each, with zero-variance groups excluded
bash scripts/runs/run_kl_sweep.sh --kl-values 0,0.01 --rollout_filter_include_zero False --gpus-per-exp 4 --gpus 0,1,2,3,4,5,6,7
```
Outputs:
- Per-value logs: `logs/kl_sweep_Qwen2.5-3B/<filter_tag>/<value_label>/`
- Summary log: `logs/kl_sweep_Qwen2.5-3B.log`
---
## 3. Entropy Sweep (`run_entropy_sweep.sh`)
Scans `actor_rollout_ref.actor.entropy_coeff` on Sokoban.
Goal:
- Isolate the effect of entropy regularization while fixing KL to `0` and keeping RV-filter effectively off with `rollout_filter_value=1`
Key Details:
- Entropy is computed token-wise over the full vocabulary on response tokens
- Aggregation is `token-mean`, so the sweep compares average token-level exploration pressure
- The entropy term enters the actor loss with a negative sign, so larger `entropy_coeff` encourages more exploration
- The script keeps `entropy_from_logits_with_chunking=True`, so large-vocabulary entropy is computed in a memory-friendly way
```bash
bash scripts/runs/run_entropy_sweep.sh
```
Options:
- `--steps` (default: `400`)
- `--entropy-values` (comma list; default: `0,0.001,0.003,0.01,0.03,0.1`)
- `--rollout_filter_include_zero` (bool; default: `True`)
- `--gpus` (comma list; auto-detect if omitted)
- `--gpus-per-exp` (default: `1`)
- `--ray-num-cpus` (default: `16`)
- `--gpu-memory-utilization` (default: `0.5`)
- `--save-freq` (default: `-1`)
Examples:
```bash
# Run the full default sweep
bash scripts/runs/run_entropy_sweep.sh
# Run two entropy points on 4xH100 each, with zero-variance groups excluded
bash scripts/runs/run_entropy_sweep.sh --entropy-values 0,0.01 --rollout_filter_include_zero False --gpus-per-exp 4 --gpus 0,1,2,3,4,5,6,7
```
Outputs:
- Per-value logs: `logs/entropy_sweep_Qwen2.5-3B/<filter_tag>/<value_label>/`
- Summary log: `logs/entropy_sweep_Qwen2.5-3B.log`
---
## Common Notes
- Comparability protocol:
- The three Sokoban sweeps change only one intervention axis at a time
- Top-p sweep scans RV-filter while fixing `use_kl_loss=True`, `kl_loss_coef=0.001`, and `entropy_coeff=0.001`
- KL sweep scans `kl_loss_coef` while keeping entropy off and filtering off
- Entropy sweep scans `entropy_coeff` while keeping KL off and filtering off
- Training budget and early stopping:
- Each condition runs for at most `400` PPO steps with `8` train env groups and `16` rollouts per group
- Runs may stop early if reward variance collapses for long enough or if validation success stays below the failure threshold for repeated validations
- Early stopping is part of the comparison protocol: if a setting stops early, that run is treated as a failed training regime rather than a fully budgeted run
- Shared setup across all three sweeps:
- Config: `_2_sokoban`
- Model: `Qwen/Qwen2.5-3B`
- `algorithm.adv_estimator=gae`
- `trainer.total_training_steps=400`
- `trainer.save_freq=-1`
- `trainer.logger=['console','wandb']`
- `trainer.val_before_train=True`
- `actor_rollout_ref.actor.filter_loss_scaling=none`
- `actor_rollout_ref.actor.ppo_mini_batch_size=32`
- `actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4`
- `critic.ppo_mini_batch_size=32`
- `critic.ppo_micro_batch_size_per_gpu=4`
- `actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8`
- `actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8`
- `es_manager.train.env_groups=8`, `es_manager.train.group_size=16`
- `es_manager.val.env_groups=512`, `es_manager.val.group_size=1`
- Rollout filter settings used by these sweeps:
- `rollout_filter_strategy=top_p`
- `rollout_filter_top_p_prob_mode=softmax`
- `rollout_filter_type=largest`
- `rollout_filter_metric=reward_variance`
- Top-p sweep uses two distinct `top_p=1.0` conditions:
- `1.0`: `rollout_filter_value=1.0`, `rollout_filter_include_zero=False`
- `nofilter`: `rollout_filter_value=1.0`, `rollout_filter_include_zero=True`
- KL sweep and Entropy sweep default to `rollout_filter_include_zero=True`; if you pass `--rollout_filter_include_zero False`, logs are written under `filter_zero/` instead of `nofilter/`
- You can run a single sweep point on `4xH100` by setting `--gpus-per-exp 4` and passing a 4-GPU list, or run two sweep points in parallel by passing an 8-GPU list
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