AGENTIC-SYSTEMSJuly 22, 20261 min read

S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF

Researchers introduced S2T-RLHF, a hierarchical credit assignment framework that stabilizes preference-based RLHF training by decomposing response-level rewards into sentence and token levels.

Computer Science > Artificial Intelligence

Title:S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF

View PDF HTML (experimental)Abstract:Reinforcement learning from human feedback (RLHF) with preference-based reward models often exhibits unstable training dynamics. A key contributing factor is that standard RLHF relies on a single sequence-level scalar reward, which is propagated to token-level policy updates and leaves credit assignment within a response inherently ambiguous. Recent work has attempted to address this issue by refining rewards into denser token-level supervision, often relying on the implicit assumption that finer-grained credit assignment improves optimization. We argue that this assumption is incomplete: when preference signals are noisy and only defined at the response level, overly fine-grained reward refinement can amplify reward uncertainty and destabilize learning. To address this problem, we propose a granularity-aware principle for hierarchical credit assignment, emphasizing stability-oriented reward design rather than maximal allocation precision. Under this principle, sentences serve as a natural intermediate granularity, balancing semantic coherence with robustness to token-level noise. Guided by this view, we introduce S2T-RLHF. This sentence-to-token reward decomposition framework first allocates sequence-level preference rewards across sentences and then applies bounded token-level refinement within each sentence, without reward-model retraining or token-level supervision. Experiments across multiple datasets and optimization settings show that S2T-RLHF improves training stability and robustness while maintaining competitive preference alignment.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis

Researchers have introduced FindStatBench, a new execution benchmark containing 2,329 tasks designed to evaluate the combinatorial code synthesis capabilities of large language models.

agentic-systems

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference

Researchers have introduced Probabilistic Concept-Aware Steering (PCS), an inference-time intervention framework for LLMs. PCS provides fine-grained, safety-oriented semantic steering while preserving original task competence.

agentic-systems

MUX: Continuous Reasoning via Multiplexed Tokens

Researchers introduced MUX, a novel latent reasoning method that compresses discrete text-based reasoning steps into continuous multiplexed tokens. By enabling lossless superposition, MUX significantly boosts LLM reasoning efficiency and speed across complex problem-solving tasks.

agentic-systems

Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems

Researchers introduce Semantic Cooperative Games (SCG) and the SLIC algorithm to evaluate agent contributions in LLM-based multi-agent systems without re-running models, reducing computational costs by 93.3%.

agentic-systems

ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data

Researchers have introduced ProbSPARQL, an upward-compatible SPARQL extension designed to query multi-dimensional and uncertain numeric measurement data within Knowledge Graphs, providing significant query performance gains for complex industrial applications.

agentic-systems

Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)

Current AI/ML research on deepfakes focuses primarily on epistemic harms like fake news and scams, leaving a dangerous gap in addressing AI-generated non-consensual intimate imagery (AIG-NCII) and its dignity harms to victims.