REVIEW 22 cited by
Direct Language Model Alignment from Online AI Feedback
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Direct alignment from preferences (DAP) methods, such as DPO, have recently emerged as efficient alternatives to reinforcement learning from human feedback (RLHF), that do not require a separate reward model. However, the preference datasets used in DAP methods are usually collected ahead of training and never updated, thus the feedback is purely offline. Moreover, responses in these datasets are often sampled from a language model distinct from the one being aligned, and since the model evolves over training, the alignment phase is inevitably off-policy. In this study, we posit that online feedback is key and improves DAP methods. Our method, online AI feedback (OAIF), uses an LLM as annotator: on each training iteration, we sample two responses from the current model and prompt the LLM annotator to choose which one is preferred, thus providing online feedback. Despite its simplicity, we demonstrate via human evaluation in several tasks that OAIF outperforms both offline DAP and RLHF methods. We further show that the feedback leveraged in OAIF is easily controllable, via instruction prompts to the LLM annotator.
Forward citations
Cited by 22 Pith papers
-
Safety Alignment of LMs via Non-cooperative Games
Jointly training an Attacker and Defender LLM in a non-zero-sum game with pairwise preference judges produces a defender with much lower jailbreak success while preserving general utility.
-
Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Function
ZSPO provably converges to a stationary policy using only the sign of preference feedback, without knowing the link function between preferences and rewards.
-
Preference learning made easy: Everything should be understood through win rate
Under two axioms (preference-consistency and prevalence-consistency), the only distribution-grounded evaluation for preference learning is h-win rate, and most popular alignment methods can be classified by whether th...
-
Multi-Turn On-Policy Distillation with Prefix Replay
ReOPD offline-distills multi-turn agentic LLMs via teacher-prefix replay plus step-decay sampling, matching online OPD accuracy at ≥4× speed with zero tool calls.
-
Noisy Pairwise-Comparison Random Search for Smooth Nonconvex Optimization
Noisy-comparison random search reaches ε-stationarity in O(k/(p²ε²)) comparisons for smooth nonconvex objectives with k-dimensional active subspace.
-
Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization
LLM attention maps reveal a preplan-and-anchor pattern, and reweighting RL credit toward the flagged tokens improves math/QA reasoning.
-
Bridging Offline and Online Reinforcement Learning for LLMs
Semi-online DPO, which syncs the generation model every few update steps, performs nearly as well as fully online DPO and GRPO, while strongly beating offline DPO.
-
Mitigating Reward Over-optimization in Direct Alignment Algorithms with Importance Sampling
Reweighting offline DPO pairs by the likelihood ratio pi_theta/pi_ref reduces reward over-optimization and keeps the model closer to the reference policy.
-
Customizing Speech Recognition Model with Large Language Model Feedback
LLM log-probability scores combined with acoustic scores serve as RL rewards to adapt ASR models to new domains without labeled data.
-
Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment
Post-training alignment on synthetic graph data yields statistically significant gains on some real-world implicit-graph tasks, but gains are inconsistent and multi-step reasoning remains fragile.
-
Online Knowledge Distillation with Reward Guidance
A preference-based knowledge distillation framework uses a confidence-set reward model in a min-max imitation game, with offline, online, and white-box variants, and outperforms prior KD baselines on LLM benchmarks.
-
MPO: Multilingual Safety Alignment via Reward Gap Optimization
MPO reduces jailbreak success in multilingual LLMs by regressing target-language reward gaps onto the English reward gap, outperforming DPO and related methods while preserving utility.
-
Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models
A generative reward model trained with long chain-of-thought and rule-based RL outperforms standard and vertically scaled reward baselines on RM-Bench and RewardBench.
-
Process Reward Models for LLM Agents: Practical Framework and Directions
A lightweight actor-critic loop trains 3B LLM agents to surpass GPT-4o on ALFWorld by learning step-level reward models from rollouts or demonstrations.
-
Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding
CoT data curated by two-round LLM prompting and VLM verification, then SFT+GRPO with fine-grained rewards, improves MapDR rule–lane association F1 from 0.642 to 0.723.
-
From Answers to Rationales: Self-Aligning Multimodal Reasoning with Answer-Oriented Chain-of-Thought
Answer-oriented chain-of-thought prompts that generate both positive and negative reasoning data, combined with iterative DPO, improve multimodal LLM reasoning on several benchmarks.
-
MOSLIM:Align with diverse preferences in prompts through reward classification
A prompt-controlled multi-objective alignment method using a multi-head classification reward model and a z-score reward mapping, claimed to work with off-the-shelf models.
-
Self-Training Large Language Models with Confident Reasoning
CORE-PO self-trains LLMs to prefer reasoning paths with high self-estimated confidence, improving answer and reasoning accuracy on several benchmarks.
-
Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion
The submitted manuscript's abstract and full text are mismatched; the claimed 3D detection method is not present in the body.
-
Data Diversification Methods In Alignment Enhance Math Performance In LLMs
DTS, which generates diverse solution strategies before writing solutions, improves GSM8K by 7.1 points and MATH by 4.2 points over an untuned base model at 1.03x baseline compute.
-
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
A survey-style reference book mapping the full agentic-AI stack from transformer internals to production deployment, with no new research result.
-
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
A tutorial reviewing LLM alignment through the lens of inverse reinforcement learning, arguing that neural reward models learned from human data are central to post-training.
Discussion (0). Sign in to comment.