Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards

• URL: http://arxiv.org/abs/2508.21476v1
• Date: Fri, 29 Aug 2025 10:00:55 GMT
• Title: Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards
• Authors: Xiaolong Wei, Bo Lu, Xingyu Zhang, Zhejun Zhao, Dongdong Shen, Long Xia, Dawei Yin,
• Abstract summary: This paper explores two distinct AI-driven reward strategies within a Reinforcement Learning from AI Feedback framework.<n>The first strategy employs a RM trained on high-quality preference data curated by a novel multi-agent rejection sampling framework.<n>The second strategy utilizes a principle-guided LLM-as-a-Judge, whose reward function is optimized via an adversarial training scheme.
• Score: 33.911792632604424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable creative writing capabilities, yet their substantial computational demands hinder widespread use. Enhancing Small Language Models (SLMs) offers a promising alternative, but current methods like Supervised Fine-Tuning (SFT) struggle with novelty, and Reinforcement Learning from Human Feedback (RLHF) is costly. This paper explores two distinct AI-driven reward strategies within a Reinforcement Learning from AI Feedback (RLAIF) framework to ignite the creative writing of a 7B-parameter SLM, specifically for generating Chinese greetings. The first strategy employs a RM trained on high-quality preference data curated by a novel multi-agent rejection sampling framework designed for creative tasks. The second, more novel strategy utilizes a principle-guided LLM-as-a-Judge, whose reward function is optimized via an adversarial training scheme with a reflection mechanism, to directly provide reward signals. Comprehensive experiments reveal that while both approaches significantly enhance creative output over baselines, the principle-guided LLM-as-a-Judge demonstrably yields superior generation quality. Furthermore, it offers notable advantages in training efficiency and reduced dependency on human-annotated data, presenting a more scalable and effective path towards creative SLMs. Our automated evaluation methods also exhibit strong alignment with human judgments. Our code and data are publicly available at https://github.com/weixiaolong94-hub/Igniting-Creative-Writing-in-Small-Language-Models.

Related papers

• Unified Reinforcement and Imitation Learning for Vision-Language Models [84.84277196012907]
  Vision-Language Models (VLMs) have achieved remarkable progress, yet their large scale often renders them impractical for resource-constrained environments.<n>This paper introduces Unified Reinforcement and Imitation Learning (RIL), a novel and efficient training algorithm designed to create powerful, lightweight VLMs.
  arXiv  Detail & Related papers  (2025-10-22T07:12:14Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• Preference-Guided Learning for Sparse-Reward Multi-Agent Reinforcement Learning [15.034714081414691]
  We study the problem of online multi-agent reinforcement learning (MARL) in environments with sparse rewards.<n>The lack of intermediate rewards hinders standard MARL algorithms from effectively guiding policy learning.<n>We propose a novel framework that integrates online inverse preference learning with multi-agent on-policy optimization.
  arXiv  Detail & Related papers  (2025-09-26T03:41:40Z)
• PUMA: Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning [54.73049408950049]
  We propose a Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning.<n>Our approach improves unified multimodal retrieval from both structural and learning perspectives.
  arXiv  Detail & Related papers  (2025-07-10T16:47:25Z)
• MeRF: Motivation-enhanced Reinforcement Finetuning for Large Reasoning Models [95.6332110724999]
  Motivation-enhanced Reinforcement Finetuning (MeRF) is an intuitive yet effective method enhancing reinforcement learning of Large Language Models (LLMs)<n>MeRF directly injects the reward specification into the prompt, which serves as an in-context motivation for model to improve its responses with awareness of the optimization objective.<n> Empirical evaluations on the Knights and Knaves(K&K) logic puzzle reasoning benchmark demonstrate that textttMeRF achieves substantial performance gains over baselines.
  arXiv  Detail & Related papers  (2025-06-23T10:37:57Z)
• LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation [72.02635550088546]
  This work explores how large language models (LLMs) can enhance CLIP's capability, especially for processing longer and more complex image captions.<n>We introduce a caption-to-caption contrastive fine-tuning framework, significantly enhancing the discriminative quality of LLM outputs.<n>Our approach outperforms LoRA-based methods, achieving nearly fourfold faster training with superior performance.
  arXiv  Detail & Related papers  (2024-11-07T18:59:16Z)
• Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning [28.077228879886402]
  Reinforcement Learning (RL) suffers from sample inefficiency in reward domains, and the problem is further pronounced in case of transitions. To improve the sample efficiency, reward shaping is a well-studied approach to introduce intrinsic rewards that can help the RL agent converge to an optimal policy faster.
  arXiv  Detail & Related papers  (2024-05-24T03:53:57Z)
• Multi-turn Reinforcement Learning from Preference Human Feedback [41.327438095745315]
  Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models with human preferences.<n>Existing methods work by emulating the preferences at the single decision (turn) level.<n>We develop novel methods for Reinforcement Learning from preference feedback between two full multi-turn conversations.
  arXiv  Detail & Related papers  (2024-05-23T14:53:54Z)
• Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation [29.6763730290473]
  Reinforcement learning can align language models with non-differentiable reward signals, such as human preferences. This paper introduces a novel framework that utilizes the critique capability of Large Language Models to produce intermediate-step rewards.
  arXiv  Detail & Related papers  (2024-01-14T22:05:11Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• SALMON: Self-Alignment with Instructable Reward Models [80.83323636730341]
  This paper presents a novel approach, namely SALMON, to align base language models with minimal human supervision. We develop an AI assistant named Dromedary-2 with only 6 exemplars for in-context learning and 31 human-defined principles.
  arXiv  Detail & Related papers  (2023-10-09T17:56:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.