Archer2.0-Code-1.5B-Preview

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✨ Archer2.0

<div> 🏹️ Reinforcement Learning for Enhanced Reasoning in LLMs 🎯 </div> </div> <div> <br> <div align="center">

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Overview

Archer2.0 marks a significant evolution from its predecessor through the introduction of Asymmetric Importance Sampling Policy Optimization (ASPO), which is designed to overcome the fundamental limitations of PPO-Clip, effectively mitigating issues like entropy collapse and repetitive outputs, preventing premature convergence, and thereby enabling more advanced reinforcement learning capabilities.

<div align="center"> <img src="archer2.0.png" width="100%"/> </div>

While our mathematical models are still in training and have not converged, we have evaluated Archer2.0 on the LiveCodeBench v5 and v6 code benchmarks. The results are detailed in the table below.

<table> <thead> <tr> <th rowspan="2">Method</th> <th colspan="2">LCB v5 (2024.08.01–2025.02.01)</th> <th colspan="2">LCB v6 (2025.02.01–2025.05.01)</th> <th rowspan="2">Avg.</th> </tr> <tr> <th>avg@8</th> <th>pass@8</th> <th>avg@16</th> <th>pass@16</th> </tr> </thead> <tbody> <tr> <td>DeepSeek-R1-1.5B</td> <td>16.7</td> <td>29.0</td> <td>17.2</td> <td>34.4</td> <td>17.0</td> </tr> <tr> <td>DAPO</td> <td>26.0</td> <td>40.5</td> <td>27.6</td> <td>43.5</td> <td>26.8</td> </tr> <tr> <td>DeepCoder-1.5B</td> <td>23.3</td> <td>39.1</td> <td>22.6</td> <td>42.0</td> <td>23.0</td> </tr> <tr> <td>Nemotron-1.5B</td> <td>26.1</td> <td>35.5</td> <td>29.5</td> <td>42.8</td> <td>27.8</td> </tr> <tr> <td><strong>Archer-Code-1.5B</strong></td> <td><strong>29.4</strong></td> <td><strong>43.7</strong></td> <td><strong>30.2</strong></td> <td><strong>45.8</strong></td> <td><strong>29.8</strong></td> </tr> <tr> <td><strong>Archer2.0-Code-1.5B-Preview</strong></td> <td><strong>31.5</strong></td> <td><strong>47.0</strong></td> <td><strong>30.5</strong></td> <td><strong>46.0</strong></td> <td><strong>31.0</strong></td> </tr> </tbody> </table>