arXiv Machine Learning
Locally-Guided Actor-Critic: Training a Goal-conditioned Actor with a Subgoal-aware Critic
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arXiv Machine Learning
Oct 8
An Informational Curse of Horizon in Goal-Conditioned Policy Learning
The paper identifies an informational curse of horizon in goal-conditioned policy learning, showing that longer goal relabeling horizons during training can hurt policy generalization and performance. Controlled experiments with oracle planners separate training horizons from test horizons, revealing that behavioral cloning policies degrade with longer horizons while reinforcement learning mitigates this effect. The authors attribute this to reduced conditional mutual information between actions and hindsight-relabeled goals, and demonstrate that distilling short-horizon policy Jacobians into long-horizon policies improves performance, especially in combinatorial manipulation tasks.
arXiv Machine Learning
Jun 25
Consistent Zero-Shot Imitation with Contrastive Goal Inference
arXiv:2510. 17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time.
arXiv AI
Sep 30
Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training
arXiv:2609.37119v1 Announce Type: cross Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instabilit...
arXiv Machine Learning
Jul 10
Beyond Success Rates: Trainability and Extractability for Offline GCRL
arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.
arXiv AI
Sep 30
Beyond a single latent space: a dual-latent world model for long-horizon planning
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
arXiv AI
Aug 5
Agentic Reinforcement Learning with Self-Distilled Reward Shaping
arXiv:2608. 03223v1 Announce Type: cross Abstract: Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying which intermediate decisions deserve credit.