Curriculum Learning
A training strategy in machine learning that mimics human educational progression from simple to complex. Models are first trained on easier tasks before gradually increasing difficulty, with the goal of improving final performance and training efficiency.
Core Facts
In sparse-reward scenarios, agents often need techniques such as curriculum learning, reward shaping, or intrinsic motivation-based exploration to converge effectively.
Curriculum Learning was systematically proposed by Bengio et al. in 2009, with the core idea of mimicking the human learning pattern of progressing 'from easy to hard'.
Curriculum Learning was proposed by Bengio et al. in 2009; its core idea is to progressively transition from simple samples to complex ones, which can accelerate model convergence and improve final performance
Timeline (last 90 days)
The competence frontier refers to the task difficulty range where the policy's current success rate is neither close to 0 nor close to 1, typically corresponding to success rates roughly between 20% and 80%.
REMORY adopts a curriculum learning strategy, restricting N to 8 tokens in early epochs and gradually relaxing it to 64 in later epochs.
The RL policy is expected to further close the performance gap with planners through the introduction of curriculum learning and other techniques, and to advance toward transferring from simulation to real hardware.
One curriculum learning implementation for addressing the drone racing takeoff challenge is to use initial takeoff velocity as the curriculum variable, gradually annealing from high initial velocity down to zero speed, while monitoring success rate to automatically adjust the curriculum progression pace.
VICO's co-evolution can be seen as a fusion of curriculum learning and self-play ideas in the vision-language modality, with the EnvRewriter playing the role of a teacher that dynamically generates questions based on the student's capability.
Existing multi-environment data selection methods tend to allocate training volume at the environment level, or rely primarily on reward-based local signals to select data, and are unable to perform fine-grained selection at the prompt-group level.
penalty_joint_pos_init gradually reduces its weight as training progresses through curriculum learning, and the penalty_slip anti-slip penalty has not yet been implemented.
In sparse-reward scenarios, agents often need techniques such as curriculum learning, reward shaping, or intrinsic motivation-based exploration to converge effectively.
Curriculum learning guides models to progressively build generalizable capabilities by arranging the difficulty and diversity of training tasks.
The Qwen3 training team adopted a progressive training strategy: first completing simple tasks, then scaling up, introducing multi-task training, and gradually increasing task difficulty.
3 more timeline events
All Facts (15)
In sparse-reward scenarios, agents often need techniques such as curriculum learning, reward shaping, or intrinsic motivation-based exploration to converge effectively.
75%VerifiedCurriculum Learning was systematically proposed by Bengio et al. in 2009, with the core idea of mimicking the human learning pattern of progressing 'from easy to hard'.
75%VerifiedCurriculum Learning was proposed by Bengio et al. in 2009; its core idea is to progressively transition from simple samples to complex ones, which can accelerate model convergence and improve final performance
65%UnverifiedThe competence frontier refers to the task difficulty range where the policy's current success rate is neither close to 0 nor close to 1, typically corresponding to success rates roughly between 20% and 80%.
50%UnverifiedREMORY adopts a curriculum learning strategy, restricting N to 8 tokens in early epochs and gradually relaxing it to 64 in later epochs.
50%UnverifiedOne curriculum learning implementation for addressing the drone racing takeoff challenge is to use initial takeoff velocity as the curriculum variable, gradually annealing from high initial velocity down to zero speed, while monitoring success rate to automatically adjust the curriculum progression pace.
50%UnverifiedVICO's co-evolution can be seen as a fusion of curriculum learning and self-play ideas in the vision-language modality, with the EnvRewriter playing the role of a teacher that dynamically generates questions based on the student's capability.
50%UnverifiedExisting multi-environment data selection methods tend to allocate training volume at the environment level, or rely primarily on reward-based local signals to select data, and are unable to perform fine-grained selection at the prompt-group level.
50%Unverifiedpenalty_joint_pos_init gradually reduces its weight as training progresses through curriculum learning, and the penalty_slip anti-slip penalty has not yet been implemented.
50%UnverifiedCurriculum learning guides models to progressively build generalizable capabilities by arranging the difficulty and diversity of training tasks.
50%UnverifiedThe Qwen3 training team adopted a progressive training strategy: first completing simple tasks, then scaling up, introducing multi-task training, and gradually increasing task difficulty.
50%UnverifiedTraining employs unrolled horizon curriculum learning, starting from K=2 and progressively doubling to 512 (2→4→8→16→32→64→128→256→512).
50%UnverifiedDynamically adjusting environment difficulty to match the agent's capability level draws on the idea of Curriculum Learning.
50%Unverified课程学习（curriculum learning）在双足行走场景中的典型设计是先学站立平衡、再外力辅助单步迈进、逐步减少辅助直到自主行走
50%UnverifiedThe RL policy is expected to further close the performance gap with planners through the introduction of curriculum learning and other techniques, and to advance toward transferring from simulation to real hardware.
50%