# Justrl Simple Recipe

> Demonstrate that simple single-stage RL with fixed hyperparameters matches complex multi-stage approaches for training small LLMs on mathematical reasoning. Use basic setup: GRPO algorithm, rule-based verification, 16K token context, standard training data without difficulty filtering. Train two 1.5B models to competitive performance using 2× less compute than sophisticated approaches.

- Skill: `adu2021/justrl-simple-recipe` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/justrl-simple-recipe`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/justrl-simple-recipe/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/justrl-simple-recipe

---


## Skill Summary

JustRL challenges prevailing assumptions about LLM training complexity. The paper demonstrates that simple, single-stage reinforcement learning with fixed hyperparameters can match or exceed complex multi-stage approaches for training small language models on mathematical reasoning. The minimal recipe uses GRPO algorithm with rule-based verification, 16K token context limit, and standard training data without difficulty filtering. Training dynamics exhibit smooth monotonic improvement without collapses or plateaus typically motivating multi-stage interventions. Two 1.5B models (DeepSeek and Nemotron backbones) achieve competitive performance (54.9% and 64.3% average accuracy respectively) while using 2× less compute.

## When To Use

- Training small language models (1-3B) on mathematical reasoning
- Projects skeptical of complex multi-stage RL schemes
- Scenarios where simple approaches reduce infrastructure complexity
- Research questioning necessity of sophisticated training techniques

## When NOT To Use

- Large-scale model training (findings may not scale beyond 1.5B)
- Tasks benefiting specifically from curriculum learning or multi-stage approaches
- Scenarios where simple methods genuinely underperform
- Domains requiring complex RL dynamics beyond basic GRPO

## Core Technique

The recipe intentionally minimizes complexity:

**1. Single-Stage Training**
No progressive context lengthening, no curriculum switching, no dynamic adjustments. Apply same training approach uniformly throughout.

**2. Fixed Hyperparameters**
Use identical hyperparameters across all models and training stages. No adaptive scheduling or dynamic adjustments based on training progress.

**3. Standard Training Data**
Use basic training data without difficulty filtering, dynamic sampling, or curriculum-based selection. Standard dataset for baseline performance.

**4. Basic Algorithm**
- GRPO: standard group policy optimization
- Rule-based verification: simple correctness checking
- 16K token context: moderate but fixed context window

## Key Insight

As authors note, the approach achieves results "while using 2× less compute than sophisticated approaches." Training dynamics exhibit smooth, monotonic improvement without collapses or plateaus typically motivating multi-stage interventions. This suggests that "adequate scale with simple methods" may suffice where the field has added unnecessary complexity.

## Results

- DeepSeek 1.5B: 54.9% average accuracy across nine benchmarks
- Nemotron 1.5B: 64.3% average accuracy
- Smooth training without catastrophic forgetting
- 2× compute efficiency vs. multi-stage approaches

## Implementation Notes

Implement basic GRPO algorithm with fixed hyperparameters. Use rule-based verification for mathematical correctness. Train with standard data without curriculum or difficulty filtering. Monitor for smooth improvement trajectory. Validate that simple approaches work before adding complexity.

## References

- Original paper: JustRL (Dec 2025)
- Group Policy Optimization (GRPO)
- Language model training recipes