aiqbench analysis
This research introduces Goal-Conditioned Supervised Learning (GCSL), an efficient offline framework that treats graded user feedback as explicit goals to align large language models without the high costs of reinforcement learning. By formulating learning around quality thresholds rather than simple imitation, the method effectively mitigates bounded-learning effects while leveraging natural-language representations for better generalization. The approach demonstrates superior performance across non-toxic generation, code synthesis, and recommendation tasks compared to standard supervised fine-tuning baselines.
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
arXiv:2605.16345v2 Announce Type: replace Abstract: Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divided into online and offline paradigms. Online methods, such as RL-based alignment, can directly optimize outcome quality but typically rely on external reward models and iterative rollouts, making them costly and difficult to deploy in many cases. Offline methods are more efficient, but prevailing approaches such as supervised fine-tuning (SFT) and direct preference optimization (DPO) remain limited: SFT typically collapses graded feedback into binary supervision, while DPO depends on paired preference data that is often unavailable or expensive to construct. In this paper, we propose goal-conditioned supervised learning (GCSL) as an offline fine-tuning framework for LLMs. Our core idea is to treat feedback signals directly as an explicit goal and train the model, purely through supervised learning, to generate responses that achieve that goal. To better exploit graded feedback, we further introduce a novel goal formulation that defines learning as consistently pursuing outcomes above a target quality threshold, rather than imitating samples from a selected high-quality subset. This design mitigates the bounded-learning effect by learning transferable patterns of meeting or exceeding quality thresholds. We also propose natural-language goal representations to further connect these patterns to the LLM's pretrained knowledge and generalization capabilities. We evaluate our method on three tasks: non-toxic generation, code generation, and LLM for recommendation. Results show that our approach consistently outperforms standard offline fine-tuning baselines while retaining the efficiency, scalability, and simple data requirements of supervised learning.
Abstract:Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divided into online and offline paradigms. Online methods, such as RL-based alignment, can directly optimize outcome quality but typically rely on external reward models and iterative rollouts, making them costly and difficult to deploy in many cases. Offline methods are more efficient, but prevailing approaches such as supervised fine-tuning (SFT) and direct preference optimization (DPO) remain limited: SFT typically collapses graded feedback into binary supervision, while DPO depends on paired preference data that is often unavailable or expensive to construct. In this paper, we propose goal-conditioned supervised learning (GCSL) as an offline fine-tuning framework for LLMs. Our core idea is to treat feedback signals directly as an explicit goal and train the model, purely through supervised learning, to generate responses that achieve that goal. To better exploit graded feedback, we further introduce a novel goal formulation that defines learning as consistently pursuing outcomes above a target quality threshold, rather than imitating samples from a selected high-quality subset. This design mitigates the bounded-learning effect by learning transferable patterns of meeting or exceeding quality thresholds. We also propose natural-language goal representations to further connect these patterns to the LLM's pretrained knowledge and generalization capabilities. We evaluate our method on three tasks: non-toxic generation, code generation, and LLM for recommendation. Results show that our approach consistently outperforms standard offline fine-tuning baselines while retaining the efficiency, scalability, and simple data requirements of supervised learning.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2605.16345 [cs.LG]
(or arXiv:2605.16345v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.16345 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 345 words
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