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Computer Science > Computation and Language

arXiv:2509.04501 (cs)
[Submitted on 2 Sep 2025]

Title:Understanding Reinforcement Learning for Model Training, and
future directions with GRAPE

Authors:Rohit Patel
View a PDF of the paper titled Understanding Reinforcement Learning
for Model Training, and future directions with GRAPE, by Rohit Patel
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    Abstract:This paper provides a self-contained, from-scratch,
    exposition of key algorithms for instruction tuning of models:
    SFT, Rejection Sampling, REINFORCE, Trust Region Policy
    Optimization (TRPO), Proximal Policy Optimization (PPO), Group
    Relative Policy Optimization (GRPO), and Direct Preference
    Optimization (DPO). Explanations of these algorithms often assume
    prior knowledge, lack critical details, and/or are overly
    generalized and complex. Here, each method is discussed and
    developed step by step using simplified and explicit notation
    focused on LLMs, aiming to eliminate ambiguity and provide a
    clear and intuitive understanding of the concepts. By minimizing
    detours into the broader RL literature and connecting concepts to
    LLMs, we eliminate superfluous abstractions and reduce cognitive
    overhead. Following this exposition, we provide a literature
    review of new techniques and approaches beyond those detailed.
    Finally, new ideas for research and exploration in the form of
    GRAPE (Generalized Relative Advantage Policy Evolution) are
    presented.

Comments:    35 pages, 1 figure
Subjects:    Computation and Language (cs.CL); Artificial
             Intelligence (cs.AI); Machine Learning (cs.LG)
MSC classes: 68T07, 68T05, 62M45, 68T50, 90C40
ACM classes: I.2.6; I.2.7
Cite as:     arXiv:2509.04501 [cs.CL]
             (or arXiv:2509.04501v1 [cs.CL] for this version)
             https://doi.org/10.48550/arXiv.2509.04501
             Focus to learn more
             arXiv-issued DOI via DataCite

Submission history

From: Rohit Patel [view email]
[v1] Tue, 2 Sep 2025 03:59:40 UTC (110 KB)
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