File size: 5,535 Bytes
bc3dbfd | 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 | # Search Environment Experiments
This doc covers the experiment scripts for the Search (HotpotQA + Dense Retrieval) environment.
## Overview
All experiments use:
- **Task**: SearchQA (HotpotQA multi-hop QA with Wikipedia dense retrieval)
- **Model**: `Qwen/Qwen2.5-3B-Instruct`
- **Algorithm**: PPO (`algorithm.adv_estimator=gae`)
- **Config**: `_9_search`
The sweep compares three rollout filtering strategies while keeping all other hyperparameters fixed.
| Experiment | Filter Strategy | Filter Value | Effective Batch | Description |
|-----------|----------------|-------------|----------------|-------------|
| No Filter | `top_p` | `1.0` | 128 | Baseline: all rollout groups kept |
| TopK 0.25 | `top_k` | `0.25` | 32 | Keep top 25% groups by reward variance |
| TopP 0.9 | `top_p` | `0.9` | ~115 | Keep groups covering 90% cumulative reward variance |
---
## Prerequisites
### 1. Prepare data
```bash
# HotpotQA train/val parquet
python scripts/prepare_search_data.py
# Wikipedia corpus + FAISS index (~74GB)
python scripts/download_search_index.py
```
### 2. Start retrieval server
The retrieval server provides dense retrieval over ~21M Wikipedia passages using E5-base-v2 + FAISS.
```bash
python scripts/retrieval/server.py \
--data_dir ./search_data/prebuilt_indices \
--port 8000 --host 127.0.0.1 \
--device cuda:0 --gpu_memory_limit_mb 6144
```
**Important**: We recommend running the retrieval server on a **dedicated GPU** not used by training, or on CPU. Sharing a GPU with vLLM rollout and training causes CUDA OOM errors due to memory contention between processes.
---
## Experiment Scripts
All experiments use `scripts/runs/run_search_benchmark.sh`.
### Experiment 1: PPO + No Filter (baseline)
No filtering — all rollout groups are used for training.
```bash
bash scripts/runs/run_search_benchmark.sh \
--algos PPO \
--filter-strategy top_p --filter-value 1.0 \
--gpus 0,1,2,3,4,5,6,7 --gpus-per-exp 8 \
--micro-batch 4 --mini-batch 64 \
--gpu-memory-utilization 0.65 \
--save-freq 20 --steps 200 \
--retrieval-port 8000
```
### Experiment 2: PPO + TopK=0.25
Keep only the top 25% of rollout groups ranked by reward variance.
```bash
bash scripts/runs/run_search_benchmark.sh \
--algos PPO \
--filter-strategy top_k --filter-value 0.25 \
--gpus 0,1,2,3,4,5,6,7 --gpus-per-exp 8 \
--micro-batch 4 --mini-batch 32 \
--gpu-memory-utilization 0.65 \
--save-freq 20 --steps 200 \
--retrieval-port 8000
```
Note: `mini-batch` is reduced to 32 because effective batch after filtering is `16 groups * 8 group_size * 0.25 = 32`. The `ppo_mini_batch_size` must not exceed this value.
### Experiment 3: PPO + TopP=0.9
Keep rollout groups covering the top 90% cumulative reward variance (softmax-weighted).
```bash
bash scripts/runs/run_search_benchmark.sh \
--algos PPO \
--filter-strategy top_p --filter-value 0.9 \
--gpus 0,1,2,3,4,5,6,7 --gpus-per-exp 8 \
--micro-batch 4 --mini-batch 64 \
--gpu-memory-utilization 0.65 \
--save-freq 20 --steps 200 \
--retrieval-port 8000
```
---
## W&B Runs
Project: [`cuhksz-gc/ragen_search_benchmark`](https://wandb.ai/cuhksz-gc/ragen_search_benchmark)
| Experiment | Run ID | Link |
|-----------|--------|------|
| PPO + No Filter | `2sbt8952` | [wandb](https://wandb.ai/cuhksz-gc/ragen_search_benchmark/runs/2sbt8952) |
| PPO + TopK=0.25 | `2h5c7kbb` | [wandb](https://wandb.ai/cuhksz-gc/ragen_search_benchmark/runs/2h5c7kbb) |
| PPO + TopP=0.9 | `tbgx0lpt` | [wandb](https://wandb.ai/cuhksz-gc/ragen_search_benchmark/runs/tbgx0lpt) |
---
## Shared Config
```yaml
# config/_9_search.yaml overrides
micro_batch_size_per_gpu: 4
ppo_mini_batch_size: 32-64 # depends on filter setting
agent_proxy:
max_turn: 5
max_actions_per_turn: 1
actor_rollout_ref:
rollout:
max_model_len: 5000 # TopK=0.25 experiment used 4000
max_num_batched_tokens: 5000 # TopK=0.25 experiment used 4000
gpu_memory_utilization: 0.65
temperature: 1
actor:
use_kl_loss: False
kl_loss_coef: 0.001
entropy_coeff: 0.001
loss_agg_mode: token-mean
filter_loss_scaling: none
es_manager:
train:
env_groups: 16
group_size: 8 # 16 * 8 = 128 rollouts per step
val:
env_groups: 256
collapse_detection:
compute_freq: 999 # effectively disabled
trainer:
total_training_steps: 200
save_freq: 20
val_before_train: True
logger: ['console', 'wandb']
```
---
## Common Notes
- **Retrieval server GPU deployment**: Place the E5 retrieval server on a **dedicated GPU** not used by training. Co-locating with training on the same GPU causes CUDA OOM due to memory contention between vLLM, training, and the E5 server process. Do not use CPU mode — during rollout, hundreds of environments issue concurrent retrieval requests (256 env groups can produce 1000+ requests), and CPU cannot keep up.
- **mini-batch size adjustment**: When using aggressive filtering (e.g., `top_k=0.25`), reduce `ppo_mini_batch_size` so it does not exceed `env_groups * group_size * filter_value`. Otherwise training fails with an assertion error.
- **max_model_len**: Default is 5000 (in `_9_search.yaml`). The TopK=0.25 experiment used 4000 to save KV cache memory; the No Filter and TopP=0.9 experiments use the default 5000.
- **Checkpoint size**: Each checkpoint is ~35GB (model + optimizer, 8 FSDP shards). With `save_freq=20` and 200 steps, expect 10 checkpoints (~350GB). Monitor disk usage and delete old checkpoints as needed.
|