Skip to main content Inicio Creadores adu2021 skillxiv online-experiential-learning-lms
online-experiential-learning-lms Improve deployed language models by learning from real-world user interactions. Extract transferable knowledge from interaction trajectories and consolidate via on-policy context distillation without needing environment access.
Ir a la instalación Skills Marketplace Descubre y explora habilidades de IA creadas por la comunidad.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Mostrar detalles del prompt Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.
npx skills add https://github.com/ADu2021/skillXiv --skill online-experiential-learning-lmsEl comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.
¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.
Descargar Zip Descargando... Ocupaciones relacionadas SOC
Basado en la clasificación ocupacional SOC
name online-experiential-learning-lms title Online Experiential Learning for Language Models version 0.0.2 engine skillxiv-v0.0.2-claude-opus-4.6 license MIT url https://arxiv.org/abs/2603.16856 keywords ["Online Learning","Deployment Experience","Knowledge Distillation","Continuous Improvement","On-Policy Learning"] description Improve deployed language models by learning from real-world user interactions. Extract transferable knowledge from interaction trajectories and consolidate via on-policy context distillation without needing environment access.
Online Experiential Learning for Language Models
Deployed language models generate valuable experiential data through real-world user interactions, yet this rich signal remains unexploited in typical offline training paradigms. Online Experiential Learning enables continuous model improvement by: (1) gathering interaction trajectories during actual deployment, (2) extracting transferable knowledge from these experiences, and (3) consolidating knowledge into model parameters via on-policy context distillation. The approach maintains on-policy consistency without requiring access to the user-side environment, enabling true deployment-driven improvement loops.
The key insight: experiential knowledge (patterns extracted from trajectories) is significantly more effective than raw trajectories, enabling efficient parameter updates from deployment data.
Core Concept
Online Experiential Learning operates through an iterative cycle:
Experience Collection — Gather interaction trajectories from deployed modelKnowledge Extraction — Identify generalizable patterns and insights from trajectoriesOn-Policy Distillation — Consolidate extracted knowledge into improved modelIterative Deployment — Improved model goes into production, generates new experiences
This creates a positive feedback loop where each generation of deployed model generates better experiences for the next iteration.
Architecture Overview
Deployment Trajectory Logger — Record user interactions and model responsesExperience Analyzer — Identify successful patterns and failure modesKnowledge Extractor — Synthesize generalizable insights from experiencesOn-Policy Distiller — Distill knowledge into student model with consistency preservationExperiential Knowledge Encoder — Represent extracted knowledge for model consumptionDeployment Manager — Orchestrate model updates and rollout cyclesGeneralization Validator — Test out-of-distribution performance
Implementation Steps
Start by setting up trajectory collection and analysis infrastructure.
from dataclasses import dataclass
from typing import List , Dict , Tuple
import
@dataclass
class
InteractionTrajectory
"""Single user interaction with deployed model."""
str
str
str
str
bool
float
str
class
TrajectoryLogger
"""Collect and store deployment trajectories."""
def
__init__
self, log_file='deployment_trajectories.jsonl'
self
self
self
'total_interactions'
0
'successful'
0
'failed'
0
'avg_response_length'
0
def
log_interaction
self, trajectory: InteractionTrajectory
"""Record single interaction."""
self
self
'total_interactions'
1
if
self
'successful'
1
else
self
'failed'
1
if
len
self
100
self
def
flush
self
"""Write buffered trajectories to disk."""
with
open
self
'a'
as
for
in
self
'query'
'response'
'feedback'
'success'
'timestamp'
'\n'
self
def
get_statistics
self
Dict
"""Return statistics on collected data."""
if
self
'total_interactions'
0
self
'success_rate'
self
'successful'
self
'total_interactions'
return
self
class
ExperienceAnalyzer
"""Extract patterns from trajectories."""
def
__init__
self
self
def
load_trajectories
self, log_file: str , num_recent=1000
"""Load trajectories from log file."""
with
open
'r'
as
for
in
self
def
analyze_success_patterns
self
List
str
"""Identify common patterns in successful interactions."""
for
in
self
if
'success'
if
not
return
for
in
'response'
for
in
self
3
0
1
sorted
lambda
1
True
return
for
in
10
def
analyze_failure_modes
self
List
Tuple
str
str
"""Identify common failure patterns."""
for
in
self
if
not
'success'
for
in
'query'
'feedback'
return
10
def
_extract_ngrams
self, words: List [str ], n: int
List
str
"""Extract n-grams from word sequence."""
for
in
range
len
1
' '
return
def
synthesize_insights
self
str
"""Generate text summarizing extracted knowledge."""
self
self
"Extracted deployment insights:\n\n"
"Success patterns:\n"
for
in
5
f" - {phrase} \n"
"\nCommon failure modes:\n"
for
in
5
f" - Query: {query[:50 ]} \n"
f" Feedback: {feedback[:50 ]} \n"
return
Now implement the on-policy distillation that consolidates extracted knowledge.
import torch
import torch.nn as nn
from torch.optim import AdamW
class ExperientialKnowledgeDistiller :
"""Distill deployed experiences into improved model."""
def __init__ (self, student_model, teacher_model=None ):
self .student = student_model
self .teacher = teacher_model
self .optimizer = AdamW(student_model.parameters(), lr=5e-6 )
def distill_from_experiences (self, trajectories: List [Dict ],
extracted_insights: str ,
num_epochs=3 ):
"""Update student model using extracted knowledge."""
for epoch in range (num_epochs):
total_loss = 0
for i, trajectory in enumerate (trajectories):
query = trajectory['query' ]
successful_response = trajectory['response' ]
success_flag = trajectory['success' ]
if success_flag:
loss = self ._compute_response_loss(query, successful_response)
else :
loss = self ._compute_correction_loss(query,
trajectory['feedback' ])
self .optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self .student.parameters(), 1.0 )
self .optimizer.step()
total_loss += loss.item()
if (i + 1 ) % 100 == 0 :
avg_loss = total_loss / (i + 1 )
print (f"Batch {i+1 } : Distillation Loss = {avg_loss:.4 f} " )
insight_loss = self ._compute_insight_alignment(extracted_insights)
self .optimizer.zero_grad()
insight_loss.backward()
self .optimizer.step()
def _compute_response_loss (self, query: str ,
successful_response: str ) -> torch.Tensor:
"""Loss for replicating successful responses."""
student_logits = self .student.forward(query, return_logits=True )
target_tokens = self .student.tokenizer.encode(successful_response)
loss = nn.functional.cross_entropy(
student_logits.view(-1 , student_logits.size(-1 )),
torch.tensor(target_tokens).view(-1 )
)
return loss
def _compute_correction_loss (self, query: str , failure_feedback: str ):
"""Loss for learning from failures."""
student_response = self .student.generate(query, max_length=100 )
similarity = self ._compute_similarity(student_response, failure_feedback)
loss = torch.tensor(similarity, dtype=torch.float32)
return loss
def _compute_insight_alignment (self, insights: str ) -> torch.Tensor:
"""Loss for aligning model behavior with extracted insights."""
insight_embedding = self .student.encode_text(insights)
alignment_loss = torch.tensor(0.0 )
return alignment_loss
def _compute_similarity (self, response1: str , response2: str ) -> float :
"""Simple text similarity (BLEU-like)."""
tokens1 = set (response1.lower().split())
tokens2 = set (response2.lower().split())
if not tokens1 or not tokens2:
return 0.0
intersection = len (tokens1 & tokens2)
union = len (tokens1 | tokens2)
return intersection / union
class OnlineExperientialLearner :
"""Full pipeline for online learning from deployment."""
def __init__ (self, model, log_file='trajectories.jsonl' ):
self .model = model
self .logger = TrajectoryLogger(log_file)
self .analyzer = ExperienceAnalyzer()
self .distiller = ExperientialKnowledgeDistiller(model)
def deployment_step (self, query: str , user_feedback: str ) -> str :
"""Single interaction during deployment."""
response = self .model.generate(query, max_length=100 )
success = len (user_feedback) > 0 and 'good' not in user_feedback.lower()
trajectory = InteractionTrajectory(
user_id='anon' ,
query=query,
model_response=response,
user_feedback=user_feedback,
success=success,
timestamp=time.time(),
model_version='v0'
)
self .logger.log_interaction(trajectory)
return response
def update_cycle (self, num_interactions=1000 ):
"""Periodic update from collected experiences."""
self .analyzer.load_trajectories(self .logger.log_file,
num_recent=num_interactions)
insights = self .analyzer.synthesize_insights()
print (insights)
trajectories = [
{
'query' : t['query' ],
'response' : t['response' ],
'feedback' : t['feedback' ],
'success' : t['success' ]
}
for t in self .analyzer.trajectories
]
print ("Distilling knowledge from experiences..." )
self .distiller.distill_from_experiences(trajectories, insights)
print ("Validating generalization..." )
self ._validate_generalization()
def _validate_generalization (self ):
"""Test that improvements generalize."""
validation_queries = self ._load_validation_set()
successes = 0
for query in validation_queries[:100 ]:
response = self .model.generate(query)
success = self ._evaluate_response(query, response)
if success:
successes += 1
success_rate = successes / min (100 , len (validation_queries))
print (f"Validation success rate: {success_rate:.1 %} " )
def _load_validation_set (self ):
"""Load held-out validation queries."""
return []
def _evaluate_response (self, query: str , response: str ) -> bool :
"""Simple evaluation (real systems use learning-to-rank)."""
return len (response) > 20
Practical Guidance Hyperparameters and When to Use:
Update cycle frequency: every 1000-10000 interactions (every 1-7 days depending on volume)
On-policy ratio: weight successful trajectories 3-5x higher than corrected failures
Insight extraction threshold: use only patterns appearing in >5% of successful interactions
Apply when you have continuous deployment generating high-volume interaction data
Particularly effective for conversational models and interactive systems
For offline-only systems without user interaction data
When deployment scenarios are drastically different from training (distribution shift)
For safety-critical applications without explicit human oversight of updates
When data collection violates privacy constraints
Distribution shift: deployment data differs from training; use domain adaptation techniques
Feedback bias: user signals may not reflect true quality; validate with held-out eval sets
Catastrophic forgetting: focus on recent experiences too much; maintain replay buffer
Positive feedback loops: successful model generates easier data, suppressing diversity; periodically reset to baseline
Privacy concerns: ensure user data is properly anonymized before extraction and distillation
Reference 