docs/experiment_intervention_sweep.md

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:

# 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:

# 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 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:

# 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