LLM Value Functions for Credit Assignment in Agentic RL
Abstract
Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assigning credit to individual actions is difficult. Methods such as GRPO assign the same trajectory-level advantage to all actions, while critic-based methods require learning an additional value function that can be costly and unstable. We introduce LLM Value Function RL (LVRL), which uses the pretrained knowledge and in-context learning ability of an LLM to estimate policy-aware state values without finetuning a separate critic. LVRL conditions the value model on group context and calibrates its predictions using known values at the initial and terminal states, enabling step-level credit assignment from estimated value differences. We further extend this idea to learning from an expert policy through on-policy distillation (OPD), introducing LLM Value Function OPD (LVOPD), which focuses teacher supervision on actions associated with decreases in estimated value. We evaluate our approach across diverse agentic benchmarks: AppWorld, BabyAI, and SWE-bench Verified. Across policy scales (4B to 32B), LVRL consistently and substantially outperforms both GRPO and PPO. On AppWorld and SWE-bench Verified, LVRL yields absolute improvements of up to 13.6 and 8.6 percentage points over GRPO. Notably, on tasks requiring extended exploration like BabyAI, LVRL bridges the gap from total failure to 75.0% success. Furthermore, LVOPD yields consistent improvements over standard distillation baselines on AppWorld. Finally, Monte-Carlo analysis confirms that our frozen critic accurately identifies semantic pitfalls and recoveries, proving that LVRL extracts robust, policy-aware step-level credit from sparse terminal outcomes.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces LVRL, a method using LLMs as value functions for credit assignment in agentic RL, achieving substantial improvements over standard RL baselines. The technical contribution is significant, offering a novel and effective way to leverage pretrained LLM capabilities for reinforcement learning, with strong empirical validation across diverse benchmarks and model scales.
Methodology
The paper proposes LVRL, a method that leverages the in-context learning capabilities of a pretrained LLM to estimate state values for credit assignment in agentic RL. By conditioning the value model on group context and calibrating predictions using known boundary values (initial and terminal states), the method avoids the instability and cost of training a separate critic network. The extension to on-policy distillation (LVOPD) is a logical and effective application of this value estimation to focus teacher supervision on critical decision points. The methodology is sound, addressing a core bottleneck in LLM agent training (sparse rewards) with a resource-efficient approach that utilizes existing model capabilities.
Results & evaluation
The evaluation is comprehensive, covering diverse agentic benchmarks (AppWorld, BabyAI, SWE-bench Verified) and multiple model scales (4B to 32B). The reported improvements over strong baselines like GRPO and PPO are substantial, particularly the transition from total failure to 75% success on BabyAI, which demonstrates the method's effectiveness in long-horizon exploration tasks. The Monte-Carlo analysis providing qualitative evidence of semantic understanding in the value estimates adds depth to the quantitative results.
Reproducibility
The paper appears to be a preprint (arXiv) with a submission to ICLR 2027. While the methodology is clearly described, the lack of a provided code repository or project URL in the extracted text limits immediate reproducibility. However, the reliance on standard LLM APIs and RL frameworks suggests that implementation is feasible for practitioners with access to similar models.
Limitations
The primary limitation is the computational cost of using a large LLM as a value estimator, which may be prohibitive for very large-scale deployments or real-time applications. Additionally, the performance gains are dependent on the quality of the base LLM's in-context learning abilities, which may vary across different model families. The paper does not extensively discuss the failure modes of the value estimation process when the LLM's prior knowledge conflicts with the specific task dynamics.
Broader impact
This work has significant implications for the field of LLM agents, offering a scalable and effective solution to the credit assignment problem. By demonstrating that pretrained LLMs can serve as effective value estimators without fine-tuning, it opens new avenues for integrating RL with LLMs more efficiently. The approach could be widely adopted in developing autonomous agents for software engineering, web navigation, and other complex interactive tasks. The paper introduces LVRL, a method using LLMs as value functions for credit assignment in agentic RL, achieving substantial improvements over standard RL baselines. The technical contribution is significant, offering a novel and effective way to leverage pretrained LLM capabilities for reinforcement learning, with strong empirical validation across diverse benchmarks and model scales.
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