credit-assignment

CounterRoute introduces an online reinforcement-learning framework that jointly learns routing and mode-conditioned responses in dual-mode language models, improving accuracy while reducing inference tokens.

SLCA-GRPO introduces Segment-Locked Credit Assignment to improve reinforcement learning for tool-calling agents by decoupling advantage estimation and using hierarchical rewards, leading to faster convergence and higher accuracy.

The paper introduces RECAP, a redundancy-aware learning method that improves the efficiency of large reasoning models by assigning credit to steps based on their structural role and efficacy, enhancing accuracy while reducing token usage.

This paper introduces Reinforcement Learning with Decomposed Subtasks (RLDS), a method that decomposes trajectory reward into per-subtask advantages to improve credit assignment in reinforcement learning for language model agents, showing significant gains on high-heterogeneity agentic benchmarks.

ReCAST proposes a method for per-reward, timestep-dependent credit assignment in diffusion model fine-tuning, separating user preferences from temporal allocation to improve alignment and informativeness.

PC-ALM is a local training method that uses layer-local dynamical systems to propagate supervision credit, enabling the training of up to 1000-layer networks without backpropagation while nearly matching its performance.

The paper proposes GACA, a granularity-adaptive credit assignment method for long-horizon LLM agent reinforcement learning that improves task success by adapting resolution to step importance.

TIGPO proposes a temporal instance-graph policy optimization method that extends graph-based credit assignment across policy updates for long-horizon LLM agents, using persistent transition graphs and revisit slots to improve advantage estimation and performance on benchmarks like ALFWorld and WebShop.

PGPO proposes potential-guided policy optimization for multi-turn agentic tasks, enabling finer-grained credit assignment in LLM post-training and showing strong results on ALFWorld and WebShop benchmarks.

MASkills presents a continual learning framework that optimizes multi-agent LLM systems through agent skills, using skill-conditioned credit assignment and hierarchical aggregation to improve performance on tasks like HotpotQA and GAIA.

CHIME is a credit-aware hierarchical memory framework that separates planning and execution memory banks to improve long-horizon agentic planning by accurately attributing task outcomes and outperforming baselines.

DRACO is a reinforcement learning method that dynamically generates rubrics and redistributes trajectory scores to improve long-horizon agent performance without verifiers.

CDPR is a counterfactual advantage-based credit assignment method for training cost-aware sequential medical diagnosis models using reinforcement learning, improving accuracy while reducing examination costs and number.

The paper introduces CREST, a method to improve multi-turn agent training by assigning separate credits to each turn and using a self-teacher to focus learning without overriding rewards, showing performance gains on benchmarks.

CBPO introduces a contrastive branch policy optimization method for fine-grained credit assignment in reinforcement learning with verifiable rewards, enhancing language model performance in tool-integrated reasoning tasks across multiple benchmarks.

Proposes HiDiffTIR, a hierarchical difficulty-aware policy optimization framework for multi-turn tool-integrated reasoning in LLM agents, improving performance and tool invocation accuracy through fine-grained credit assignment.

The paper proposes computation-conditioned credit transport (CCT) and the CompPO algorithm for architecture-aware credit assignment in large language model reinforcement learning, showing improved performance over baselines like GRPO.

MileGPO proposes a method for credit assignment in long-horizon LLM agents using milestone inference with local evidence, achieving state-of-the-art performance on ALFWorld and WebShop benchmarks.

This paper audits step-level credit assignment in LLM agents using causal ground truth from executed replay, finding that common credit signals fail to identify causally important steps better than chance, with implications for training methods.

This paper introduces Feedback-Aware Credit Assignment (Faca) to improve multi-turn tool-using language agents by using next-turn user reactions as local credit signals, showing significant performance gains on interactive benchmarks.