Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21282 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918313974562816 |
|---|---|
| author | Dwyer, Madeleine Sobey, Adam Chapman, Adriane |
| author_facet | Dwyer, Madeleine Sobey, Adam Chapman, Adriane |
| contents | Training large language models (LLMs) with reinforcement learning (RL) methods such as PPO and GRPO commonly relies on ratio clipping to stabilise updates. While effective at preventing instability, clipping discards information, introduces gradient discontinuities and can prevent exploration of better policies. Inspired by label smoothing, we propose Probability Smoothing Policy Optimisation (PSPO). PSPO smooths current policy probabilities toward the behaviour policy before computing importance ratios, creating a soft trust region that preserves gradients while preventing destabilising updates. Unlike prior soft clipping approaches that use sigmoid-based transformations which can suffer from vanishing gradients and saturation, our method uses a linear interpolation, providing simpler and more robust gradient preservation.
Empirically, GR-PSPO outperforms clipping and sigmoid-based alternatives on mathematical reasoning benchmarks when refining models with prior domain knowledge, achieving an accuracy of 79.9% on GSM8K and 59.6% on MATH for Qwen2-Math-1.5B. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | It's Not You, It's Clipping: A Soft Trust-Region via Probability Smoothing for LLM RL Dwyer, Madeleine Sobey, Adam Chapman, Adriane Machine Learning Artificial Intelligence Training large language models (LLMs) with reinforcement learning (RL) methods such as PPO and GRPO commonly relies on ratio clipping to stabilise updates. While effective at preventing instability, clipping discards information, introduces gradient discontinuities and can prevent exploration of better policies. Inspired by label smoothing, we propose Probability Smoothing Policy Optimisation (PSPO). PSPO smooths current policy probabilities toward the behaviour policy before computing importance ratios, creating a soft trust region that preserves gradients while preventing destabilising updates. Unlike prior soft clipping approaches that use sigmoid-based transformations which can suffer from vanishing gradients and saturation, our method uses a linear interpolation, providing simpler and more robust gradient preservation. Empirically, GR-PSPO outperforms clipping and sigmoid-based alternatives on mathematical reasoning benchmarks when refining models with prior domain knowledge, achieving an accuracy of 79.9% on GSM8K and 59.6% on MATH for Qwen2-Math-1.5B. |
| title | It's Not You, It's Clipping: A Soft Trust-Region via Probability Smoothing for LLM RL |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.21282 |