A Note on Hybrid Online Reinforcement and Imitation Learning for LLMs: Formulations and Algorithms
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908736879067136 |
|---|---|
| author | Li, Yingru Li, Ziniu Liu, Jiacai |
| author_facet | Li, Yingru Li, Ziniu Liu, Jiacai |
| contents | We present a unified framework for Large Language Model (LLM) fine-tuning that integrates Imitation Learning and Reinforcement Learning. By analyzing the gradient of a composite objective combining trajectory-level KL divergence with task rewards, we derive a natural decomposition into two components: (1) an analytically computable Dense Gradient for token-level imitation, and (2) a Monte Carlo estimated Sparse Gradient for long-horizon reward optimization. The Dense Gradient admits a closed-form logit-level formula, enabling efficient GPU implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Note on Hybrid Online Reinforcement and Imitation Learning for LLMs: Formulations and Algorithms Li, Yingru Li, Ziniu Liu, Jiacai Machine Learning Artificial Intelligence Computation and Language We present a unified framework for Large Language Model (LLM) fine-tuning that integrates Imitation Learning and Reinforcement Learning. By analyzing the gradient of a composite objective combining trajectory-level KL divergence with task rewards, we derive a natural decomposition into two components: (1) an analytically computable Dense Gradient for token-level imitation, and (2) a Monte Carlo estimated Sparse Gradient for long-horizon reward optimization. The Dense Gradient admits a closed-form logit-level formula, enabling efficient GPU implementation. |
| title | A Note on Hybrid Online Reinforcement and Imitation Learning for LLMs: Formulations and Algorithms |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2512.23097 |