Metadata-Version: 2.4
Name: erl-trainer
Version: 0.2.2
Summary: Experiential Reinforcement Learning (ERL) — a thin wrapper on HuggingFace TRL's GRPOTrainer implementing the ERL algorithm with reflection, memory, and internalization.
Project-URL: Repository, https://github.com/akarim23131/erl_trainer
Author: Abid Karim
License: MIT License
        
        Copyright (c) 2025 Abid Karim
        
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License-File: LICENSE
Keywords: erl,grpo,language-models,reflection,reinforcement-learning,trl
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: datasets
Requires-Dist: peft
Requires-Dist: torch>=2.0.0
Requires-Dist: transformers>=4.50.0
Requires-Dist: trl<0.18.0,>=0.17.0
Description-Content-Type: text/markdown

# erl-trainer

**Experiential Reinforcement Learning (ERL)** — a thin wrapper on HuggingFace TRL's `GRPOTrainer` that adds a reflection-retry-internalization loop to standard GRPO training.

Install it, swap `GRPOTrainer` for `ERLTrainer`, add a `feedback_func`, and you get ERL training. Everything else — LoRA, quantization, datasets, reward functions — works exactly like TRL.

## Installation

```bash
pip install erl-trainer
```

## Quick Start

```python
from erl import ERLConfig, ERLTrainer
from datasets import load_dataset
from peft import LoraConfig  # optional

dataset = load_dataset("your_dataset", split="train")

# Standard reward function (same as TRL)
def reward_func(prompts, completions, **kwargs):
    return [compute_your_score(c) for c in completions]

# NEW: Textual feedback function (unique to ERL)
def feedback_func(prompts, completions, **kwargs):
    return [get_your_feedback(c) for c in completions]

config = ERLConfig(
    output_dir="erl-output",
    num_train_epochs=3,
    learning_rate=1e-6,
    per_device_train_batch_size=4,
    num_generations=4,
    # ERL-specific params
    reward_threshold=1.0,
    memory_size=50,
    memory_top_k=3,
    internalization_coef=1.0,
)

# Optional: LoRA config (works exactly like TRL)
lora_config = LoraConfig(
    r=64,
    lora_alpha=64,
    target_modules="all-linear",
)

trainer = ERLTrainer(
    model="Qwen/Qwen2.5-3B-Instruct",
    args=config,
    train_dataset=dataset,
    reward_funcs=reward_func,
    feedback_func=feedback_func,   # NEW: the only addition vs TRL
    peft_config=lora_config,       # optional, same as TRL
)

trainer.train()
```

## The ERL Algorithm

Each training step runs seven phases:

| Phase | Description |
|-------|-------------|
| **1. First attempt** | Generate responses `y1` for the batch; compute numerical reward `r1` and textual feedback `f1`. These are two separate signals — `r1` drives the RL math, `f1` explains *why* the attempt failed. |
| **2. Gating** | Samples where `r1 < reward_threshold` enter the reflection loop; others are done. No wasted compute on already-successful attempts. |
| **3. Self-reflection** | For gated samples, the model reflects using all five inputs: the original prompt, `y1`, `f1`, `r1`, and relevant entries retrieved from cross-episode memory. Produces a natural-language improvement plan `Δ`. |
| **4. Second attempt** | The model generates `y2` conditioned on the original prompt and `Δ` only (not `y1` or `f1`). Reward `r2` is computed against the original task. |
| **5. Memory update** | If `r2 > threshold`, the reflection `Δ` is stored in a FIFO cross-episode memory. Future steps retrieve the most recent stored reflections to seed the reflection prompt. |
| **6. GRPO update** | Policy gradient over the combined batch — `y1` (reward `r1`), `Δ` (reward `r2`), and `y2` (reward `r2`) — in one joint update. Negative advantage pushes the model away from bad outputs; positive advantage reinforces good ones. |
| **7. Internalization** | SFT cross-entropy on `(original_prompt → y2)` pairs for successful second attempts (`r2 > 0`). Trains the model to produce the improved answer directly from `x`, without any reflection scaffold at inference time. |

Reflections and retries are generated by the **same model with the same weights** as the first attempt — no freezing, no separate model. All generation happens before the optimizer step.

## Configuration

All `GRPOConfig` options are inherited. ERL adds:

| Parameter | Default | Description |
|-----------|---------|-------------|
| `reward_threshold` | `1.0` | Gating threshold τ. Samples with `r1 >= τ` skip reflection. |
| `memory_size` | `50` | Max reflections stored in cross-episode memory. |
| `memory_top_k` | `3` | Reflections retrieved per reflection prompt. |
| `reflection_system_prompt` | *(built-in)* | Template with `{prompt}`, `{attempt}`, `{feedback}`, `{reward}`, `{memory}`. |
| `retry_system_prompt` | *(built-in)* | Template with `{prompt}` and `{reflection}`. |
| `internalization_coef` | `1.0` | Weight of internalization loss relative to RL loss. |
| `enable_memory` | `True` | Toggle cross-episode memory on/off. |
| `enable_internalization` | `True` | Toggle the distillation step on/off. |

## Feedback Function

The only new concept vs TRL is `feedback_func`. It receives the same keyword arguments as a reward function and must return a list of strings — one per completion:

```python
def feedback_func(prompts, completions, **kwargs) -> list[str]:
    feedbacks = []
    for prompt, completion in zip(prompts, completions):
        feedbacks.append(f"Your answer was missing: {diagnose(prompt, completion)}")
    return feedbacks
```

`feedback_func` may be `None`. In that case, empty strings are passed to the reflection prompt — training still runs, but reflection quality will be lower since the model has no textual diagnosis to work from.

## Implementation Notes

### TRL version compatibility

`erl-trainer 0.2.x` targets **TRL 0.17.x** exclusively.

In TRL 0.17.0 the monolithic `_generate_and_score_completions` method handles everything — tokenisation, generation, EOS masking, log-probability computation, reward evaluation, advantage normalisation, and metrics logging — and returns a plain dict consumed by `_compute_loss`. There is no separate `_generate`, `_calculate_rewards`, or `_get_per_token_logps_and_entropies`.

`ERLTrainer` overrides `_generate_and_score_completions` and delegates Phase 1 (first attempt) entirely to the parent. ERL phases 2–7 run on top of the parent's output. The returned dict has the same keys as the parent's method so `_compute_loss` works without modification.

### Batched reflection and retry generation

Reflections and retries for all gated samples in a batch are generated in two batched `model.generate` calls (one for all reflections, one for all retries), not one call per sample. This keeps GPU utilisation high regardless of how many samples are gated.

### Advantage update

Because TRL 0.17.0's `_compute_loss` reads advantages directly from the returned dict, ERL injects its corrected advantages at the dict level before returning:

- **Non-gated samples** keep their y1 reward in the advantage computation.
- **Gated samples** have their reward replaced with the y2 reward (the second-attempt score).

The combined reward tensor is then group-normalised using the same formula as the parent, so the advantage scale is consistent across the batch.

### Algorithm 1 vs Algorithm 2

This implementation follows **Algorithm 1** (simplified) from the paper: advantages are derived from a combined reward tensor (y1 rewards for non-gated, y2 rewards for gated) and the GRPO update runs once over the y1 completions with those corrected advantages.

The paper's Appendix A describes **Algorithm 2** (full), which runs two separate RL updates — one on `y1` alone, then one on `Δ + y2`. Algorithm 2 can be approximated by calling `compute_loss` twice with different inputs.

### Compatibility

| erl-trainer | TRL | transformers |
|-------------|-----|--------------|
| 0.2.x | 0.17.x | ≥ 4.50.0 |
| 0.1.x | 0.15.x | ≥ 4.50.0 |

When a new TRL minor version is released, we verify compatibility and update the version constraint.

### Ablations

Both memory and internalization can be disabled independently, which is useful for ablation studies:

```python
config = ERLConfig(
    enable_memory=False,          # no cross-episode memory
    enable_internalization=False, # no distillation step, pure RL
    ...
)
```

## Citation

If you use `erl-trainer` in your research, please cite the original ERL paper and TRL:

**ERL paper** — the algorithm this package implements:

```bibtex
@article{shi2026erl,
  title   = {Experiential Reinforcement Learning},
  author  = {Shi, Taiwei and Chen, Sihao and Jiang, Bowen and Song, Linxin and Yang, Longqi and Zhao, Jieyu},
  journal = {arXiv preprint arXiv:2602.13949},
  year    = {2026},
  url     = {https://arxiv.org/abs/2602.13949}
}
```

**TRL** — the GRPO trainer this package extends:

```bibtex
@software{vonwerra2020trl,
  title  = {{TRL: Transformers Reinforcement Learning}},
  author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and
            Beeching, Edward and Thrush, Tristan and Lambert, Nathan and
            Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  url    = {https://github.com/huggingface/trl},
  year   = {2020}
}
```

## License

MIT
